software engineer

ai engineer

ai/ml engineer

assistant chief engineer Jobs in New York

assistant chief engineer

chief engineer

ai ml engineer

Tata Consultancy Services (TCS)

<div></div><div><div><div><div><div>Must Have Technical/Functional Skills</div><br>
<div><br>
</div><div>Technical Skills
</div><ul><li>4&ndash;8+ years in ML engineering with production deployments.
</li></ul><ul><li>Strong Python expertise (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
</li></ul><ul><li>Hands-on with Amazon SageMaker Pipelines &amp; Feature Store.
</li></ul><ul><li>Experience building containerized ML solutions (Docker).
</li></ul><ul><li>Knowledge of API deployment (REST/gRPC).
</li></ul><ul><li>SQL expertise for insurance data modeling.</li></ul><br>
<div><br>
</div><div>AWS Expertise
</div><ul><li>Deep experience in:
</li></ul><ul><li>SageMaker model training &amp; deployment
</li></ul><ul><li>IAM policies &amp; VPC configurations
</li></ul><ul><li>S3 lifecycle policies
</li></ul><ul><li>Infrastructure as Code (CloudFormation / Terraform)</li></ul><br>
<div><br>
</div><div>Roles &amp; Responsibilities</div><br>
<div><br>
</div><div>Key Responsibilities</div><br>
<div><br>
</div><div>ML Model Development &ndash; Insurance Use Cases
</div><ul><li>Build predictive models for:
</li></ul><ul><li>Claims severity &amp; frequency prediction
</li></ul><ul><li>Fraud detection (SIU support models)
</li></ul><ul><li>Underwriting risk scoring
</li></ul><ul><li>Loss ratio forecasting
</li></ul><ul><li>Policy lapse prediction
</li></ul><ul><li>Implement NLP models for claims notes, adjuster reports, and policy documents.
</li></ul><ul><li>Develop computer vision models for damage assessment (auto/property insurance).</li></ul><br>
<div><br>
</div><div>MLOps &amp; Production Deployment
</div><ul><li>Design end-to-end ML pipelines using:
</li></ul><ul><li>Amazon SageMaker (training, pipelines, endpoints, feature store)
</li></ul><ul><li>AWS Lambda
</li></ul><ul><li>Amazon S3
</li></ul><ul><li>Amazon ECR
</li></ul><ul><li>Amazon ECS / Amazon EKS
</li></ul><ul><li>Implement CI/CD for ML using:
</li></ul><ul><li>AWS CodePipeline
</li></ul><ul><li>AWS CodeBuild
</li></ul><ul><li>Manage model versioning, retraining workflows, and rollback strategies.
</li></ul><ul><li>Deploy real-time and batch inference endpoints.
</li></ul><ul><li>Implement model monitoring (data drift, concept drift, bias detection).</li></ul><br>
<div><br>
</div><div>Security, Risk &amp; Compliance
</div><ul><li>Implement secure ML architecture aligned to insurance regulatory requirements.
</li></ul><ul><li>Encrypt data at rest and in transit.
</li></ul><ul><li>Document model governance and validation processes for actuarial and compliance review.</li></ul><br>
<div><br>
</div><div>Required Qualifications
</div><div>Technical Skills
</div><ul><li>4&ndash;8+ years in ML engineering with production deployments.
</li></ul><ul><li>Strong Python expertise (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
</li></ul><ul><li>Hands-on with Amazon SageMaker Pipelines &amp; Feature Store.
</li></ul><ul><li>Experience building containerized ML solutions (Docker).
</li></ul><ul><li>Knowledge of API deployment (REST/gRPC).
</li></ul><ul><li>SQL expertise for insurance data modeling.</li></ul><br>
<div><br>
</div><div>TCS Employee Benefits Summary:
</div><div>Discretionary Annual Incentive.
</div><div>Comprehensive Medical Coverage: Medical &amp; Health, Dental &amp; Vision, Disability Planning &amp; Insurance, Pet Insurance Plans.
</div><div>Family Support: Maternal &amp; Parental Leaves.
</div><div>Insurance Options: Auto &amp; Home Insurance, Identity Theft Protection.
</div><div>Convenience &amp; Professional Growth: Commuter Benefits &amp; Certification &amp; Training Reimbursement.
</div><div>Time Off: Vacation, Time Off, Sick Leave &amp; H olidays.
</div><div>Legal &amp; Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.</div><br>
<div><br>
</div><div>Salary Range: $120,000 - $140,000 a year
</div></div></div></div><div><div><div><b>Location</b></div>
<div>New York, NY</div>
</div><div><div><b>Job Function</b></div>
<div>TECHNOLOGY</div>
</div><div><div><b>Role</b></div>
<div>Senior Engineer</div>
</div><div><div><b>Job Id</b></div>
<div>402530</div>
</div><div><div><b>Desired Skills</b></div>
<div>AWS | Machine Learning</div>
</div><div><div><b>Salary Range</b></div>
<div>$120,000-$140,000 a year</div>
</div></div><p>Desired Candidate Profile
</p><div><div><b>Qualifications</b> : BACHELOR OF COMPUTER SCIENCE</div></div></div><div></div>

AI/ML Engineer

MetLife

<div><div><div><div><div><div>Location(s)
</div><div><ul><li>Posting Location: New York, New York</li></ul><br>
</div></div></div></div></div><div><div><div><div><div>City/Cities
</div><div>New York
</div></div><div><div>Region/States
</div><div>New York
</div></div><div><div>Country
</div><div>United States
</div></div><div><div>Working Schedule
</div><div>Full-Time
</div></div><div><div>Work Arrangement
</div><div>Hybrid
</div></div><div><div>Travel Required
</div><div>10%
</div></div><div><div>Relocation Assistance Available
</div><div>No
</div></div><div><div>Posted Date
</div><div>19-Mar-2026
</div></div><div><div>Job ID
</div><div>16370
</div></div></div></div></div><div><div><div><h3 class="jobSectionHeader"><b>Description and Requirements
</b></h3></div></div><div><div><div><div><div><p><b>The Team You Will Join</b></p>
<p>MetLife is on a transformational journey into the New Frontier. Working closely with our business partners, we are embarking on a transformation that will drive a holistic and cohesive product-aligned model, enabled by an end-to-end delivery capability to drive our highest impact enterprise strategic agenda. Within MetLife Global Technology &ndash; Corporate Systems - Controls, our team assists our IT and business partners in solving complex operational and risk management challenges by rapidly delivering lasting transformations with speed and discipline. Driven by passion and purpose, you will be instrumental in driving change and implementing MetLife&rsquo;s product model for our businesses while aligning our brand and technology to bring immediate value to our customers and partners.</p><p><br>
</p><p><b>The Opportunity</b></p>
<p>MetLife Finance &amp; Risk Management Technology &ndash; Controls IT is seeking a Senior Data Engineering &amp; AI/ML Engineer to play a critical role in building and modernizing data and analytics solutions that support Risk, Compliance, and Internal Audit functions.</p>
<p>The ideal candidate brings strong hands‑on engineering expertise, coupled with the ability to collaborate, communicate, and lead across product, engineering, and business teams. This role requires deep experience in data engineering, system integration, and AI/ML enablement, along with a solid understanding of engineering best practices and developer enablement.</p>
<p>This position is both hands‑on and strategic, with responsibilities that include guiding teams through data modeling, architecture and design decisions, platform modernization, and agile delivery practices.</p><p><br>
</p><p><b>How You&rsquo;ll Help Us Build a Confident Future (Key Responsibilities)</b></p>
<ul><li>Partner closely with product owners and stakeholders to plan, prioritize, and deliver high‑impact business outcomes aligned with enterprise risk and control objectives.</li>
<li>Lead the design, development, testing, and deployment of scalable data engineering and data processing pipelines.</li>
<li>Define and maintain architecture, design, and technical documentation to support long‑term platform sustainability.</li>
<li>Oversee operational support for complex data systems and workflows, including root cause analysis and issue resolution.</li>
<li>Champion and promote engineering best practices across Data Engineering, Data Platforms, AI/ML, and Cloud technologies.</li>
<li>Provide technical leadership and guidance to teams on data modeling, integration patterns, performance optimization, and modernization initiatives.</li>
<li>Support agile delivery by translating business needs into clear technical requirements, user stories, and prioritized backlogs.</li></ul><br>
<p></p><p><b>Required Skills</b></p>
<ul><li>Bachelor&rsquo;s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.</li>
<li>Strong proficiency in Python and SQL, with deep experience in database design, query optimization, and performance tuning across large‑scale data systems.</li>
<li>5+ years of hands‑on experience building and optimizing data engineering pipelines, leveraging ETL platforms (e.g., Informatica, Pentaho), and/or Modern data platforms (e.g., Databricks, Snowflake, Hadoop).</li>
<li>5+ years of practical experience with AI/ML technologies and libraries, such as PySpark, Pandas, NumPy, LangChain, Hugging Face, and LLM/RAG integration and prompt engineering.</li>
<li>3+ experience working with GenAI / Large Language Models (LLMs) such as OpenAI and Copilot, and integration via API.</li>
<li>Experience in data modeling (dimensional, canonical, or domain‑oriented models) and designing analytics‑ready datasets and semantic layers.</li>
<li>Hands‑on experience with Agile delivery frameworks and DevOps tooling (e.g., Azure DevOps, GitHub, Jira).</li>
<li>Excellent verbal and written communication skills, with the ability to build strong partnerships across technical and business stakeholders.</li></ul><br>
<p></p><p><b>Preferred Skills</b></p>
<ul><li>Hands‑on experience in custom software development within a cloud‑based DevOps environment, including familiarity with CI/CD automation.</li>
<li>Experience implementing data quality checks, validation frameworks, lineage, and governance controls in regulated environments.</li>
<li>Hands‑on experience designing and operating data solutions on cloud platforms (preferably Azure), including storage, compute, and networking fundamentals.</li>
<li>Familiarity with Enterprise Risk Management, Compliance, Internal Audit, or other GRC domains.</li>
<li>Experience within Financial Services or a global insurance organization.</li></ul><br>
</div></div></div><p></p><div><div><div>Location Expectation: This is a hybrid role<b> requiring a minimum of 3 days per week </b>in office.</div>
</div></div><div><div><div>The expected salary range for this position is $103,300 - $154,975. This role may also be eligible for annual short-term incentive compensation. All incentives and benefits are subject to the applicable plan terms.</div>
</div></div></div></div></div><div><div><div><div><div><b>Benefits We Offer</b><div><br>
Our U.S. benefits address holistic well-being with programs for physical and mental health, financial wellness, and support for families. We offer a comprehensive health plan that includes medical/prescription drug and vision, dental insurance, and no-cost short- and long-term disability. We also provide company-paid life insurance and legal services, a retirement pension funded entirely by MetLife and 401(k) with employer matching, group discounts on voluntary insurance products including auto and home, pet, critical illness, hospital indemnity, and accident insurance, as well as Employee Assistance Program (EAP) and digital mental health programs, parental leave, volunteer time off, tuition assistance and much more!</div>
</div></div><div><div><b>About MetLife</b><div><p><br>
Recognized on Fortune magazine's list of the &quot;World's Most Admired Companies&quot;, Fortune World&rsquo;s 25 Best Workplaces&trade;, as well as the Fortune 100 Best Companies to Work For&reg;, MetLife, through its subsidiaries and affiliates, is one of the world&rsquo;s leading financial services companies; providing insurance, annuities, employee benefits and asset management to individual and institutional customers. With operations in more than 40 markets, we hold leading positions in the United States, Latin America, Asia, Europe, and the Middle East.
</p><p></p><p>Our purpose is simple - to help our colleagues, customers, communities, and the world at large create a more confident future. United by purpose and guided by our core values - Win Together, Do the Right Thing, Deliver Impact Over Activity, and Think Ahead - we&rsquo;re inspired to transform the next century in financial services. At MetLife, it&rsquo;s #AllTogetherPossible. Join us!</p></div><br>
</div></div></div></div></div><div></div><div><div><div><div><div><br>
<i>MetLife is an Equal Opportunity Employer. All employment decisions are made without regards to race, color, national origin, religion, creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, marital or domestic/civil partnership status, genetic information, citizenship status (although applicants and employees must be legally authorized to work in the United States), uniformed service member or veteran status, or any other characteristic protected by applicable federal, state, or local law (&ldquo;protected characteristics&rdquo;).</i><div></div><div><i><br>
If you need an accommodation due to a disability, please email us at accommodations@metlife.com. This information will be held in confidence and used only to determine an appropriate accommodation for the application process.</i><br>
<br>
<i>MetLife maintains a drug-free workplace.</i></div>
</div></div><div><div>$103,300 - $154,975</div></div></div></div></div></div>

Senior Data & AI Engineer

Prolific

<div>
 <ul>
  <li>
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  </li>
  <li>
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   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  AI Trainer &ndash; Machine Learning Specialists
  <br/>
  <br/>
 </strong>
 <strong>
  About Prolific
  <br/>
  <br/>
 </strong>
 Prolific is not just another player in the AI space &ndash; we are building the biggest pool of quality human data in the world.
 <br/>
 <br/>
 Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.
 <br/>
 <br/>
 <em>
  The role
  <br/>
  <br/>
 </em>
 We're looking for
 <strong>
  AI Trainer &ndash; Machine Learning Specialists
 </strong>
 to help train and evaluate cutting-edge AI models using real ML expertise. If you have the necessary experience, we'll send you a quick
 <strong>
  10- to 15-minute test
 </strong>
 to assess your skills and suitability for AI tasks. If successful, you'll be invited to join Prolific as a participant, where you'll get paid to train and evaluate powerful AI models.
 <br/>
 <br/>
 Researchers looking for your skills tend to pay up to
 <strong>
  $150/hr per AI task completed
 </strong>
 . You must be prepared to complete paid tasks that require
 <strong>
  one hour of uninterrupted work
 </strong>
 , though many are shorter.
 <br/>
 <br/>
 What you'll bring
 <br/>
 <br/>
 <ul>
  <li>
   AI Training task skills and verifiable professional experience as a Machine Learning Specialist (e.g., ML engineer, data scientist, applied scientist, research engineer)
  </li>
  <li>
   A willingness to take our skills verification test to assess your suitability for our participant pool
  </li>
  <li>
   Strong attention to detail and the ability to concentrate on complex tasks for up to one hour at a time
  </li>
  <li>
   A reliable and fast internet connection and access to a computer
  </li>
  <li>
   A willingness to self-declare your earnings, as our participants are self-employed
  </li>
  <li>
   A Paypal account to receive payment from our clients
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  What you'll be doing in the role
  <br/>
  <br/>
 </em>
 <ul>
  <li>
   Completing AI training tasks such as analyzing, editing, and writing annotations (including technical reasoning and structured evaluation)
  </li>
  <li>
   Judging the performance of AI in performing ML-relevant tasks (e.g., model/experiment critique, data leakage detection, metrics interpretation, debugging approach, methodology review)
  </li>
  <li>
   Improving cutting-edge AI models by providing expert feedback on correctness, robustness, clarity, and technical depth
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  Key Technologies
  <br/>
  <br/>
 </em>
 <ul>
  <li>
   General AI Training
  </li>
  <li>
   Model Evaluation and Evals
  </li>
  <li>
   Trust and Safety
  </li>
  <li>
   Red Teaming
  </li>
  <li>
   Quality Analytics
  </li>
  <li>
   Data/ML Concepts (e.g., supervised learning, deep learning, NLP, CV, statistics, experimentation)
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  Why Prolific is a great platform to join as a Participant
  <br/>
  <br/>
 </em>
 Joining our platform as a Prolific participant will give you the chance to influence the AI models of the future using professional machine learning expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home.
 <br/>
 <br/>
 We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.
 <br/>
 <br/>
 We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation &ndash; one that reflects the breath and the best of humanity.
 <br/>
 <br/>
 <em>
  Links to more information on Prolific
  <br/>
  <br/>
 </em>
 Website
 <br/>
 <br/>
 Youtube
 <br/>
 <br/>
 Privacy Statement
 <br/>
 <br/>
 By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.
</div>


AI Training - Machine Learning Specialist (EST)

Capital One

<div>Senior Lead AI Engineer<p></p><p><b>
Overview:</b></p><p></p><p>
At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent &mdash; along with our deep experience in machine learning &mdash; position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI &amp; ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.</p><p></p><p><b>
Team Description:</b></p><p></p><p>
The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact.</p><p><b><br>
In this role, you will:</b></p><p></p><ul><li><p>
Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.</p></li></ul><p></p><ul><li><p>
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc.</p></li></ul><p></p><ul><li><p>
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.</p></li></ul><p></p><ul><li><p>
Invent and introduce state-of-the-art LLM optimization techniques to improve the performance &mdash; scalability, cost, latency, throughput &mdash; of large scale production AI systems.</p></li></ul><p></p><ul><li><p>
Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.</p></li></ul><p></p><p><b>
The Ideal Candidate:</b></p><p></p><ul><li><p>
You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.</p></li></ul><p></p><ul><li><p>
Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.</p></li></ul><p></p><ul><li><p>
You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven.</p></li></ul><p></p><ul><li><p>
You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss.</p></li></ul><p></p><ul><li><p>
You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.</p></li></ul><p></p><p><b>
Basic Qualifications:</b></p><p></p><ul><li><p>
Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies</p></li><li><p>
At least 6 years of experience programming with Python, Go, Scala, or Java</p></li></ul><p></p><p><b>
Preferred Qualifications:</b></p><ul><li><p>
7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)</p></li><li><p>
Experience designing, developing, integrating, delivering, and supporting complex AI systems</p></li><li><p>
Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders</p></li><li><p>
Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang</p></li><li><p>
Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost</p></li><li><p>
Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production</p></li><li><p>
Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers</p></li></ul><p></p><p><i><br>
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.</i></p><p></p><p>
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.</p><p></p>
Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer<br>
<p></p>
McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer<br>
<p></p>
New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer<br>
<p></p>
San Jose, CA: $250,800 - $286,200 for Sr. Lead AI Engineer<p></p><p><br>
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.</p><p></p>
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.<p></p><p>
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.</p><p></p>
This role is expected to accept applications for a minimum of 5 business days.<p></p>
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.<p></p><p>
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.</p><p></p><p>
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com</p><p></p><p>
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.</p><p></p><p>
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p></div>

Senior Lead AI Engineer

Fusemachines

<div>
 <strong>
  About Fusemachines
  <br/>
  <br/>
 </strong>
 Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients&rsquo; AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail, manufacturing, and government.
 <br/>
 <br/>
 Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
 <br/>
 <br/>
 <strong>
  Salary Range:
 </strong>
 US$ 140,000-190,000/year
 <br/>
 <br/>
 <strong>
  Role Overview
  <br/>
  <br/>
 </strong>
 We&rsquo;re hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You&rsquo;ll work across the ML lifecycle&mdash;from problem framing and data exploration to model development, evaluation, deployment, and monitoring&mdash;often in partnership with client stakeholders and internal delivery teams.
 <br/>
 <br/>
 You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Problem Framing &amp; Stakeholder Partnership
  </li>
  <ul>
   <li>
    Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.)
   </li>
   <li>
    Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability)
   </li>
  </ul>
  <li>
   Data Analysis &amp; Feature Engineering
  </li>
  <ul>
   <li>
    Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses
   </li>
   <li>
    Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices
   </li>
   <li>
    Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions
   </li>
  </ul>
  <li>
   Model Development (Core ML)
  </li>
  <ul>
   <li>
    Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data)
   </li>
   <li>
    Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation
   </li>
   <li>
    Build time series models (statistical and ML/DL approaches) and validate with proper backtesting
   </li>
   <li>
    Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness
   </li>
   <li>
    Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making
   </li>
  </ul>
  <li>
   Deep Learning
  </li>
  <ul>
   <li>
    Build and train deep learning models using PyTorch or TensorFlow/Keras
   </li>
   <li>
    Use best practices for training (regularization, calibration, class imbalance handling, reproducibility, sound train/val/test design)
   </li>
  </ul>
  <li>
   Evaluation, Explainability, and Iteration
  </li>
  <ul>
   <li>
    Choose appropriate metrics (AUC/F1/PR, RMSE/MAE/MAPE, calibration, lift, and business KPIs) and create evaluation reports
   </li>
   <li>
    Perform error analysis and interpretation (feature importance/SHAP, cohort slicing) and iterate based on evidence
   </li>
  </ul>
  <li>
   Productionization &amp; MLOps (Project-Dependent)
  </li>
  <ul>
   <li>
    Package models for deployment (batch scoring pipelines or real-time APIs) and collaborate with engineers on integration
   </li>
   <li>
    Implement practical MLOps: versioning, reproducible training, automated evaluation, monitoring for drift/performance, and retraining plans
   </li>
  </ul>
  <li>
   Documentation &amp; Communication
  </li>
  <ul>
   <li>
    Communicate tradeoffs and recommendations clearly to technical and non-technical stakeholders
   </li>
   <li>
    Create documentation and lightweight demos that make results actionable
    <br/>
   </li>
  </ul>
 </ul>
 Success in This Role Looks Like
 <br/>
 <br/>
 <ul>
  <li>
   You deliver models that perform well and move business metrics (revenue lift, cost reduction, risk reduction, improved forecast accuracy, operational efficiency)
  </li>
  <li>
   Your work is reproducible and production-aware: clear data lineage, robust evaluation, and a credible path to deployment/monitoring
  </li>
  <li>
   Stakeholders trust your judgment in selecting methods and communicating uncertainty honestly
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   3&ndash;8 years of experience in data science, machine learning engineering, or applied ML (mid-to-senior)
  </li>
  <li>
   Strong Python skills for data analysis and modeling (pandas/numpy/scikit-learn or equivalent)
  </li>
  <li>
   Strong SQL skills (joins, window functions, aggregation, performance awareness)
  </li>
  <li>
   Solid foundation in statistics (hypothesis testing, uncertainty, bias/variance, sampling) and practical experimentation mindset
  </li>
  <li>
   Hands-on experience across multiple model types, including:
  </li>
  <ul>
   <li>
    Classification &amp; regression
   </li>
   <li>
    Time series forecasting
   </li>
   <li>
    Clustering/segmentation
   </li>
  </ul>
  <li>
   Experience with deep learning in PyTorch or TensorFlow/Keras
  </li>
  <li>
   Strong problem-solving skills: ability to work with ambiguous goals and messy data
  </li>
  <li>
   Clear communication skills and ability to translate analysis into decisions
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience with Databricks for applied ML (e.g., Spark, Delta Lake, MLflow, Databricks Jobs/Workflows)
  </li>
  <li>
   Experience deploying models to production (APIs, batch pipelines) and maintaining them over time (monitoring, retraining)
  </li>
  <li>
   Experience with orchestration tools (Airflow, Prefect, Dagster) and modern data stacks (Snowflake/BigQuery/Redshift/Databricks)
  </li>
  <li>
   Experience with cloud platforms (AWS/GCP/Azure/IBM) and containerization (Docker)
  </li>
  <li>
   Experience with responsible AI and governance best practices (privacy/PII handling, auditability, access controls)
  </li>
  <li>
   Consulting or client-facing delivery experience
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Certifications (Strong Plus)
  <br/>
  <br/>
 </strong>
 Candidates with at least one relevant certification are especially encouraged to apply:
 <br/>
 <br/>
 <ul>
  <li>
   Cloud certifications: AWS, Google Cloud, Microsoft Azure, or IBM (data/AI/ML tracks)
  </li>
  <li>
   Databricks certifications (Data Scientist, Data Engineer, or related)
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Nice-to-Have
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Causal inference experience (e.g., quasi-experimental methods, propensity scores, uplift/heterogeneous treatment effects, experimentation beyond A/B tests)
  </li>
  <li>
   Agentic development experience: designing and evaluating agentic workflows (tool use, planning, memory/state, guardrails) and integrating them into products
  </li>
  <li>
   Deep familiarity with agentic coding tools and workflows for accelerated product development (e.g., AI-assisted IDEs, code agents, automated testing/refactoring, repo-aware assistants), including strong judgment on quality, security, and maintainability
   <br/>
   <br/>
  </li>
 </ul>
 <em>
  <em>
   Fusemachines is an Equal Opportunities Employer, committed to diversity and inclusion. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or any other characteristic protected by applicable federal, state, or local laws.
   <br/>
   <br/>
  </em>
 </em>
 Important: Immigration Sponsorship Policy
 <br/>
 <br/>
 Fusemachines is unable to proceed with candidates who require any form of work authorization or immigration support from the company. This restriction applies to all types of support, including:
 <br/>
 <br/>
 <ul>
  <li>
   Direct Company Sponsorship: Such as H-1B, J-1, or TN visas
  </li>
  <li>
   Employer of Record: Listing Fusemachines as the immigration employer on any government documentation
  </li>
  <li>
   Written Documentation: Providing letters or other support for any work authorization (e.g., OPT, STEM OPT, CPT)
   <br/>
   <br/>
  </li>
 </ul>
 Powered by JazzHR
 <br/>
 <br/>
 I0GDC3XJwj
</div>


Data Scientist

FanDuel

<div>
 <strong>
  THE POSITION
  <br/>
  <br/>
 </strong>
 Our roster has an opening with your name on it
 <br/>
 <br/>
 At FanDuel, data is the heartbeat of our organization. As a Machine Learning Engineer at FanDuel, you will help us unlock the full potential of our vast amounts of real-time and relational data. You will be asked to provide our business with insight and our customers with world-class personalized experiences. Every click our users make, every bet, every touchdown, every fumble, and every play is fair game for us to turn into a stream of knowledge. Your expertise will be used here to make better and faster decisions &ndash; outpacing our competition.
 <br/>
 <br/>
 Collaboration is at the core of your role. You&rsquo;ll be the linchpin between engineering teams working downstream to build out our online application and upstream to land necessary data for feature engineering. You&rsquo;ll also be working with Data Scientists and Analysts to productionize, analyze, and validate AI powered insights. You will be asked to help organize, model, and present our data as a coherent product and offer it to our stakeholders, providing a common information framework that allows FanDuel to intelligently react to what is happening on the field and in the marketplace.
 <br/>
 <br/>
 We are looking for Senior Machine Learning Engineer who may be looking to make the move to a big data environment. If this describes you, read on &ndash; we want to hear from you!
 <br/>
 <br/>
 <strong>
  THE GAME PLAN
  <br/>
  <br/>
 </strong>
 Everyone on our team has a part to play
 <br/>
 <br/>
 <ul>
  <li>
   Designing and implementing intelligent search system incorporating typeahead search, vector search and ML personalization model signals to optimize relevance and user experience
  </li>
  <li>
   Contributing to the design and development of scalable serving systems for ML and GenAI/LLM models
  </li>
  <li>
   Developing platform features and capabilities (e.g. CLI, SDK, Infra Automation, Platform Applications) for streamlining ML Model and GenAI/LLM Application development and deployment lifecycle
  </li>
  <li>
   Business intelligence tools (e.g., Tableau, Knime, Looker)
  </li>
  <li>
   Data security and privacy (e.g. GDPR, CPP)
  </li>
  <li>
   Data governance and data testing frameworks
  </li>
  <li>
   Continuous integration and delivery of production data products
  </li>
  <li>
   An inclusive culture that expects excellence and priorities your growth as an engineer and your well-being as a person
  </li>
  <li>
   Advance your career within well-defined, skill-based tracks, either as an individual contributor or as a manager &ndash; both providing equal opportunities for compensation and advancement
  </li>
  <li>
   Collaborating with peers and sharing best practices in system reliability, automation, and data quality
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 ML engineering is a rapidly changing field &ndash; most of all, we&rsquo;re looking for someone who enjoys experimenting, keeping their finger on the pulse of current data engineering tools, and always thinking about how to do something better.
 <br/>
 <br/>
 <strong>
  THE STATS
  <br/>
  <br/>
 </strong>
 What we're looking for in our next teammate
 <br/>
 <br/>
 <ul>
  <li>
   5-7+ years of relevant experience developing code in one or more core programming languages (Python, Java, etc.)
  </li>
  <li>
   1+ Years of Experience implementing vector search, semantic search, or embedding-based retrieval systems for production ML or AI applications
  </li>
  <li>
   1+ Years of Experience working with typeahead / autocomplete systems and integrating ML signals into query understanding or ranking workflows
  </li>
  <li>
   1+ Years of Experience combining outputs from multiple retrieval systems (e.g., vector search + typeahead + personalization models) to improve relevance
  </li>
  <li>
   1+ Years of experience in deploying ML and GenAI/LLM models under the constraints of scalability, correctness, and maintainability.
  </li>
  <li>
   Hands on experience with ML frameworks and libraries (Scikit-learn, Pytorch, Tensorflow, LightGBM, Keras, MLFlow etc.) and familiarity with LLM-specific frameworks (e.g., LangChain, Hugging Face Transformers, etc).
  </li>
  <li>
   Hands on experience with one or more ML and GenAI/LLM cloud services (Amazon SageMaker, Amazon Bedrock, Databricks Mosaic AI, Seldon, Arize, etc)
  </li>
  <li>
   4+ Years of experience designing and building various software architecture, with some emphasis on scalable architectures supporting both traditional ML and advanced LLM workflows.
  </li>
  <li>
   Deep understanding and knowledge of data structures, distributed computing, and software engineering principles
  </li>
  <li>
   3+ Years of experience demonstrating technical leadership working with teams, owning projects, defining, and setting technical direction for projects.
  </li>
  <li>
   Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis, Terraform, Airflow)
  </li>
  <li>
   Ability to share findings in easy to consume formats, whether that is through dashboards or data modeling.
  </li>
  <li>
   Conduct regular design process reviews and ensure development standards within the team.
  </li>
  <li>
   Working with leadership to drive adoption of ML and GenAI/LLM solutions to product engineering teams.
  </li>
  <li>
   Experience working in a cloud environment such as AWS, GCP, Azure.
  </li>
  <li>
   Experience with Databricks is a plus, their unity catalog, another plus.
  </li>
  <li>
   Designing and building data pipelines for production level ML and GenAI/LLM infrastructure.
  </li>
  <li>
   Experience with vector databases to efficiently manage and retrieve embeddings for LLM applications, enabling high-performance similarity search and retrieval-augmented generation (RAG) workflows is a plus
  </li>
  <li>
   Motivate junior engineers on best practices and latest industry design patterns.
   <br/>
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  About Fanduel
  <br/>
  <br/>
 </strong>
 FanDuel Group is the premier mobile gaming company in the United States and Canada. FanDuel Group consists of a portfolio of leading brands across mobile wagering including: America&rsquo;s #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; the industry&rsquo;s unquestioned leader in horse racing and advance-deposit wagering, FanDuel Racing; and its daily fantasy sports product.
 <br/>
 <br/>
 In addition, FanDuel Group operates FanDuel TV, its broadly distributed linear cable television network and FanDuel TV+, its leading direct-to-consumer OTT platform. FanDuel Group has a presence across all 50 states, Canada, and Puerto Rico.
 <br/>
 <br/>
 The company is based in New York with US offices in Los Angeles, Atlanta, and Jersey City, as well as global offices in Canada and Scotland. The company&rsquo;s affiliates have offices worldwide, including in Ireland, Portugal, Romania, and Australia.
 <br/>
 <br/>
 FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator with a portfolio of globally recognized brands and traded on the New York Stock Exchange (NYSE: FLUT).
 <br/>
 <br/>
 <strong>
  Player Benefits
  <br/>
  <br/>
 </strong>
 We treat our team right
 <br/>
 <br/>
 We offer amazing benefits above and beyond the basics. We have an array of health plans to choose from (some as low as $0 per paycheck) that include programs for fertility and family planning, mental health support, and fitness benefits. We offer generous paid time off (PTO &amp; sick leave), annual bonus and long-term incentive opportunities (based on performance), 401k with up to a 5% match, commuter benefits , pet insurance, and more - check out all our benefits here:
 <strong>
  FanDuel Total Rewards
 </strong>
 . *Benefits differ across location, role, and level.
 <br/>
 <br/>
 FanDuel is an equal opportunities employer and we believe, as one of our principles states, &ldquo;We are One Team!&rdquo;. As such, we are committed to equal employment opportunity regardless of race, color, ethnicity, ancestry, religion, creed, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or any other characteristic protected by state, local or federal law. We believe FanDuel is strongest and best able to compete if all employees feel valued, respected, and included.
 <br/>
 <br/>
 <strong>
  <em>
   The applicable salary range for this position is $138,000 - $173,000 USD, which is dependent on a variety of factors including relevant experience, location, business needs and market demand. This role may offer the following benefits: medical, vision, and dental insurance; life insurance; disability insurance; a 401(k) matching program; among other employee benefits. This role may also be eligible for short-term or long-term incentive compensation, including, but not limited to, cash bonuses and stock program participation. This role includes paid personal time off and 14 paid company holidays. FanDuel offers paid sick time in accordance with all applicable state and federal laws.
   <br/>
   <br/>
  </em>
 </strong>
 <strong>
  <em>
   FanDuel is committed to providing reasonable accommodations for qualified individuals with disabilities. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please email Benefits@fanduel.com.
   <br/>
   <br/>
  </em>
 </strong>
 <strong>
  <em>
   It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
  </em>
 </strong>
</div>


Senior Machine Learning Engineer

<div>Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)<p></p><div><div><div><div><div><div><div><div><div><p>
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You&rsquo;ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You&rsquo;ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.</p><p></p><p>
Enterprise Platforms Technology (EPTech) comprises many of Capital One&rsquo;s most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices.</p><p></p><p><b>
What you&rsquo;ll do in the role:</b></p><ul><li><p>
The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:</p></li><li><p>
Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</p></li><li><p>
Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</p></li><li><p>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</p></li><li><p>
Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</p></li><li><p>
Retrain, maintain, and monitor models in production.</p></li><li><p>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</p></li><li><p>
Construct optimized data pipelines to feed ML models.</p></li><li><p>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</p></li><li><p>
Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</p></li><li><p>
Use programming languages like Python, Scala, or Java.</p></li></ul><p></p><p><b>
Basic Qualifications:</b></p><ul><li><p>
Bachelor&rsquo;s degree</p></li><li><p>
At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</p></li><li><p>
At least 4 years of experience programming with Python, Scala, or Java</p></li><li><p>
At least 3 years of experience building, scaling, and optimizing ML systems</p></li><li><p>
At least 2 years of experience leading teams developing ML solutions</p></li></ul><p></p><p><b>
Preferred Qualifications:</b></p><ul><li><p>
Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field</p></li><li><p>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</p></li><li><p>
4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</p></li><li><p>
3+ years of experience developing performant, resilient, and maintainable code</p></li><li><p>
3+ years of experience with data gathering and preparation for ML models</p></li><li><p>
3+ years of people management experience</p></li><li><p>
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</p></li><li><p>
3+ years of experience building production-ready data pipelines that feed ML models</p></li><li><p>
Ability to communicate complex technical concepts clearly to a variety of audiences</p></li></ul><p></p><p><b><i>
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
</i></b></p></div></div></div></div></div></div></div></div></div><p></p><p>The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.</p><p></p>
McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer<br>
<p></p>
New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer<p></p><p><br>
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate&rsquo;s offer letter.</p><p></p>
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.<p></p><p>
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.</p><p></p>
This role is expected to accept applications for a minimum of 5 business days.<p></p>
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City&rsquo;s Fair Chance Act; Philadelphia&rsquo;s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.<p></p><p>
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.</p><p></p><p>
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com</p><p></p><p>
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.</p><p></p><p>
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p></div>

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)

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