machine learning infrastructure engineer

Lightning AI

<div><div><h2 class="jobSectionHeader">Who We Are</h2>

Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems&mdash;designed to take ideas from research to production with less friction.

Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.

We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.
<h2 class="jobSectionHeader">
The Way We Work</h2>

The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:
<ul>
<li>Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.</li>
<li>Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.</li>
<li>Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.</li>
<li>Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.</li>
<li>Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.</li>
<li>Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.</li>
</ul>
</div><div></div><h2 class="jobSectionHeader">What We're Looking For</h2>

Lightning AI is looking to hire an AI Platform Support Engineer to join our US Customer Experience team, supporting ML engineers running large-scale training and inference workloads across cloud infrastructure, Kubernetes, and GPU platforms in production environments.

This role sits at the intersection of ML systems, cloud infrastructure, Kubernetes, and customers. You'll support engineers training models, deploying inference systems, and scaling GPU workloads in production.You are not a ticket router or traditional support engineer. You are a technical partner to ML teams - helping diagnose failures, improve reliability, and guide customers through complex distributed systems problems.

The problems range from Kubernetes scheduling and GPU orchestration to distributed PyTorch failures, inference latency, networking bottlenecks, storage performance, and platform reliability. You'll gain exposure to a wide variety of real world AI workloads across industries and help shape the infrastructure powering the next generation of ML applications.

This role is hybrid out of our Seattle, San Francisco, or New York office hubs, with an in-office requirement of at least 2 days per week and occasional team and company offsites. The role follows a Monday&ndash;Friday schedule, with working hours from 8:00 AM to 5:00 PM PST. We are not able to provide visa sponsorship for this role at this time.<h2 class="jobSectionHeader"> 

What You'll Do</h2>

Work Directly With ML Engineers
<ul>
<li>Partner directly with customer engineering teams running training and inference workloads in production</li>
<li>Help customers diagnose and resolve complex distributed systems and ML infrastructure issues</li>
<li>Act as a technical advisor during high impact incidents and platform degradation events</li>
<li>Translate infrastructure level issues into actionable guidance for ML engineers</li>
<li>Build credibility with customers through strong technical reasoning and clear communication</li>
</ul>
Debug ML Infrastructure &amp; Distributed Workloads
<ul>
<li>Investigate failures involving distributed training, Kubernetes orchestration, GPU allocation, networking, and storage systems</li>
<li>Troubleshoot PyTorch, CUDA, NCCL, and inference serving related issues</li>
<li>Analyze logs, metrics, traces, and system behavior to isolate root causes</li>
<li>Debug containerized workloads running across Kubernetes and bare metal GPU environments</li>
<li>Support customers scaling workloads across multi node GPU systems</li>
<li>Diagnose performance bottlenecks involving compute, memory, networking, or storage</li>
</ul>
Improve Reliability &amp; Platform Operations
<ul>
<li>Identify recurring patterns across customer issues and drive long term reliability improvements</li>
<li>Contribute to post incident reviews and operational improvements</li>
<li>Build internal tooling, automation, documentation, and runbooks</li>
<li>Partner closely with infrastructure, networking, and platform engineering teams</li>
<li>Help improve observability, operational visibility, and troubleshooting workflows</li>
<li>Improve the customer experience through better processes and technical guidance</li>
</ul>
What This Role Is Not

To set clear expectations:
<ul>
<li>This is not a traditional help desk or ticket routing support role</li>
<li>This is not purely customer success or account management</li>
<li>This is not a backend engineering role</li>
<li>This is not a passive escalation position</li>
</ul>
This role is for engineers who enjoy solving difficult technical problems while working closely with other engineers. 

<h2 class="jobSectionHeader">
What You'll Need</h2>
<h3 class="jobSectionHeader">Required Qualifications</h3>
<h4 class="jobSectionHeader">Infrastructure &amp; Systems</h4>
<ul><li>Strong software engineering and systems troubleshooting background</li>
<li>Experience with Kubernetes and containerized environments</li>
<li>Linux systems knowledge, including networking, storage, process management, and performance tuning</li>
<li>Experience with cloud infrastructure and distributed systems</li>
<li>Experience with observability and debugging tools such as Prometheus, Grafana, or OpenTelemetry</li>
</ul><h4 class="jobSectionHeader">ML Infrastructure Experience</h4>
<ul><li>Hands on experience operating machine learning workloads in production or research environments</li>
<li>Experience with distributed ML systems and tooling such as PyTorch, CUDA, or NCCL</li>
<li>Familiarity with GPU infrastructure and orchestration</li>
<li>Experience troubleshooting performance, reliability, or scaling issues in ML infrastructure</li>
<li>Understanding of the operational challenges involved in running ML systems at scale</li>
</ul><h4 class="jobSectionHeader">Collaboration</h4>
<ul><li>Strong communication skills and ability to work directly with highly technical customers and engineering teams</li>
<li>Comfortable operating in fast moving, highly ambiguous environments</li>
<li>Enjoys solving complex technical problems collaboratively</li>
</ul><h3 class="jobSectionHeader">Ideal Experience</h3>
<ul><li>Experience with large scale model training or distributed inference systems</li>
<li>Familiarity with Ray, Kubeflow, Slurm, or similar distributed scheduling platforms</li>
<li>Experience with InfiniBand, RDMA, or high-performance networking</li>
<li>Experience operating bare metal infrastructure</li>
<li>Familiarity with storage systems commonly used in ML environments</li>
<li>Experience working at an AI infrastructure, cloud, MLOps, or developer tooling company</li>
<li>Contributions to platform engineering, developer infrastructure, or operational tooling projects</li>
<li>Experience writing automation, tooling, or scripts in Python or similar languages</li>
</ul><div><h2 class="jobSectionHeader">Benefits and Perks</h2>

We offer a comprehensive and competitive benefits package designed to support our employees' health, well-being, and long-term success:
<ul>
<li>Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents.</li>
<li>Meaningful Equity: RSUs that give employees a stake in the company's long-term success.</li>
<li>Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.).</li>
<li>Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance.</li>
<li>Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge.</li>
<li>Paid Parental &amp; Family Leave: Paid leave to support you and your family through life's important moments.</li>
<li>Professional Development: Annual learning and development allowance to support your professional growth.</li>
<li>Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being.</li>
<li>Sabbatical Program: Four weeks of paid sabbatical leave after four years of service.</li>
<li>Flexible Work: Flexible schedules and a hybrid work model for our office-based teams.</li>
<li>In-Office Meals: Complimentary meals at our office hubs.</li>
</ul>
Benefits may vary by location, team, and role. 


At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.</div></div>

AI Platform Support Engineer (US)

Immersion Consulting, LLC

<div>Description:

The Senior Data Engineer is responsible for designing, implementing, maintaining, and optimizing a cloud-based data architecture and data pipeline ecosystem. The position supports advanced analytics, machine learning operations, fraud detection initiatives, and investigative activities by delivering scalable, secure, and sustainable Azure-based data solutions. The Senior Data Engineer develops and maintains modern ELT/ETL pipelines, data models, source-controlled environments, and operational standards that enable efficient data ingestion, processing, storage, and access.<ul><li>
Design, implement, and maintain scalable Azure-based data architecture supporting audits, investigations, and fraud analytics.</li><li>
Develop, optimize, and sustain ELT/ETL pipelines within Azure Synapse Analytics and Azure Machine Learning environments.</li><li>
Migrate and integrate large-scale datasets into Azure Data Lake Storage (ADLS).</li><li>
Establish source control, version management, and development standards across data engineering assets.</li><li>
Implement pipeline monitoring, validation, logging, and error-handling frameworks.</li><li>
Design and maintain data models, data dictionaries, entity relationship diagrams, and architectural documentation</li><li>
Optimize ingestion, transformation, storage, and retrieval performance across diverse data sources and formats.</li><li>
Develop self-service data access capabilities for analysts and investigators.</li><li>
Collaborate with Data Scientists to ensure infrastructure effectively supports machine learning and AI initiatives.</li><li>
Author and maintain Standard Operating Procedures (SOPs) governing data pipeline development, deployment, and monitoring.</li><li>
Evaluate emerging AI-enabled engineering tools and LLM-assisted automation capabilities.</li><li>
Recommend and implement architectural improvements that increase efficiency, reliability, security, and cost effectiveness.</li></ul>
Requirements:
<ul><li>Bachelor's degree in Data Engineering, Computer Science, Data Science, Machine Learning, Mathematics, or related discipline; or 5 years of relevant applied experience.</li><li>
Five or more years of experience maintaining SQL database environments and performing advanced SQL/T-SQL operations.</li><li>
Five or more years of experience designing and maintaining cloud-based ELT/ETL solutions.</li><li>
Three or more years of experience working with Azure Synapse Analytics and Azure Machine Learning.</li><li>
Three or more years of experience developing data solutions using Python and Pandas.</li><li>
Experience supporting modern data platforms and cloud-native analytics architectures.</li><li>
Demonstrated expertise in data architecture design, pipeline optimization, and operational support.</li></ul></div>

Senior Data Engineer

J2B GLOBAL LLC

<div><div><div><div><h3 class="jobSectionHeader">Principal ML Engineer</h3>
</div><div>C2CContract
</div></div><div>Chicago, IL
</div><div>Visa: US CitizenH1BTNGreen CardGC-EADH4-EADEAD
</div><div>Role: Principal ML Engineer Location: Dallas, TX - 5 days onsite from day 1 Secondary locations: Chicago, IL and New York City, NY Job Description: Responsibilities: Contribute to Mail Intelligence Mission: Support the development of AI/ML capabilities that enhance user personalization and insights within Mail and the broader client's ecosystem. Innovate and Personalize: Assist in building scalable AI/ML solutions that identify user interests and habits, contributing to personalized user experiences. Technical Leadership: Guide and mentor a team of passionate engineers, fostering a culture of innovation and excellence. Your leadership will ensure the delivery of high-quality, user-centric solutions. Collaborative Development: Work with cross-functional teams to integrate new AI/ML features and improve existing platforms, ensuring smooth user interactions. Data-Driven Insights: Apply machine learning techniques and big data processing to derive insights from mail data, enhancing content and user knowledge. Tool and Workflow Development: Develop AI/ML tools and workflows to manage and utilize large datasets, supporting AI-based infrastructure and analytics. A/B Testing and Analysis: Run regular A/B tests, gather data, and draw conclusions on the impact of your models. Commitment to Excellence: Maintain high engineering standards, focusing on code quality and architectural skills, while delivering optimal user experiences. Feedback and Recovery: Implement feedback loops and recovery paths to handle AI failures gracefully, ensuring user satisfaction. Balance Tradeoffs: Manage cost, quality, and speed tradeoffs in AI/ML solutions to optimize performance. A lot about YOU Educational Background: You have a strong foundation in computer science, data science, or a related field, with a focus on machine learning and AI technologies. Technical Skills: Proficient in programming languages such as Python or Java and machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Skilled with AI tools such as Cursor, Copilot, and Claude. AI Skills: Advanced AI skills, including experience with AI development tools and frameworks. Problem-Solving Ability: You possess strong analytical skills and a knack for solving complex problems using data-driven approaches. Collaboration: You are a team player who can work effectively with cross-functional teams, including tech leads and architects, to deliver high-quality solutions. Passion for Innovation: You are enthusiastic about leveraging AI/ML to drive innovation and enhance user experiences across Mail and the broader ecosystem. Attention to Detail: You maintain high standards for code quality and are committed to delivering robust, scalable solutions. Continuous Learning: You are eager to stay updated with the latest advancements in AI/ML and apply them to improve products and services. Qualifications Educational Requirement: Bachelor's degree in Computer Science, Data Science, or a related field; a Master's degree is a plus. Experience: 8+ years of experience in machine learning, data analysis, or a related field. Technical Proficiency: Expertise in Python, Java, or C++ and machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn. Skilled with AI tools such as Cursor, Copilot, and Claude. Data and Infrastructure: Proficient in big data technologies (Hadoop, Spark) and modern ML infrastructure (Airflow, MLFlow, feature stores, vector databases, CI/CD for ML). Generative AI and Optimization: Hands-on experience with LLMs, generative AI (prompt engineering, Agent frameworks, fine-tuning, RAG, LLMOps), and GPU optimizations. Observability and Feedback: Familiar with observability, telemetry, and user feedback loops for product improvement. Algorithm Knowledge: Solid understanding of machine learning algorithms, natural language processing, and data mining techniques. Problem-Solving Skills: Demonstrated ability to solve complex problems and implement efficient, scalable solutions. Collaboration Skills: Ability to work effectively in a team environment and collaborate with tech leads, architects, and cross-functional teams. Communication Skills: Strong verbal and written communication skills to articulate complex technical concepts to non-technical stakeholders. Continuous Improvement: Eagerness to learn and adapt to new technologies and methodologies in the AI/ML domain. Preferred Qualifications Mail Experience: Prior experience working with email systems or related technologies is a plus. Personal Attributes: Self-driven and detail-oriented, with a passion for tackling challenges. Strong teamwork spirit, excellent communication skills, and the ability to multitask and manage expectations effectively. Cloud Experience: Experience with public cloud platforms, especially Google Cloud Platform (GCP), is preferred.</div></div><div> Refer</div></div>

Principal ML Engineer

Occam Solutions

<div>Primary Responsibility 
The DevSecOps Engineer shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment. 
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services. 
Duties and Responsibilities

The DevSecOps Engineer shall:
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul> 


Minimum Qualifications

The DevSecOps Engineer shall meet the requirements for the &quot;DevSecOps Engineer &ndash; Intermediate&quot; labor category as defined in the CHESS ITES-3S contract vehicle and possess the following additional qualifications:
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within AWS GovCloud, Azure Government, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under FedRAMP High, DoD Impact Level 5 (IL5), or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li> 
</ul>
Qualifications
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

Arrow Electronics

<div>
 
 Position
 
 
 
 Data Scientist / Senior Machine Learning Engineer
 
 
 
 Job Description
 
 
 
 
 Position Overview:
 
 
 
 As Data Scientist / Senior Machine Learning Engineer at Arrow Electronics, you will assume a pivotal role in driving our data science and machine learning initiatives to reach unprecedented heights. Collaborating closely with cross-functional multinational teams, your responsibilities will encompass the end-to-end machine learning lifecycle, including stakeholder communications, data analysis, designing, development, deployment, and optimization of cutting-edge models aimed at addressing intricate challenges and generating actionable insights. Your expertise in data analysis, model development, and deployment will be crucial in shaping the future of our company.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Machine Learning Model Development: Design, develop, and implement advanced machine learning models and algorithms to solve real-world problems in various domains.
 </li>
 <li>
 Data Analysis: Explore and analyze large datasets to extract meaningful insights and identify trends, anomalies, and patterns.
 </li>
 <li>
 Model Evaluation: Evaluate the performance of machine learning models using appropriate metrics and fine-tune models for optimal results.
 </li>
 <li>
 Deployment: Lead the deployment of machine learning models into production environments and ensure scalability and efficiency.
 </li>
 <li>
 Cross-functional Collaboration: Collaborate closely with business stakeholders, software engineers, data engineers, and domain experts to integrate machine learning solutions into our products and services.
 </li>
 <li>
 Mentorship: Provide guidance and mentorship to junior data scientists and machine learning engineers, fostering a culture of continuous learning and growth.
 </li>
 <li>
 Research and Innovation: Stay up-to-date with the latest advancements in the field of data science and machine learning and apply innovative techniques to solve complex challenges.
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Proven experience (2 to 5 years) in developing and deploying machine learning models in a production environment.
 </li>
 <li>
 Proficiency in programming languages, especially Python, and experience with data-related libraries such as pandas, matplotlib, NumPy, etc.
 </li>
 <li>
 Strong knowledge of machine learning frameworks and libraries.
 </li>
 <li>
 Expertise in data preprocessing, feature engineering, and model selection.
 </li>
 <li>
 Familiarity with large language models and AI agent design.
 </li>
 <li>
 Experience with text-based NLP, information extraction, entity recognition, and text-related data science tasks is highly desirable.
 </li>
 <li>
 Strong communication skills and the ability to convey technical concepts to non-technical stakeholders.
 </li>
 <li>
 Experience with vectorized databases and unstructured search engines is a plus.
 </li>
 <li>
 A Master&rsquo;s degree in computer science, Statistics, Data Science, or a related field is a plus.
 </li>
 <li>
 Publications or contributions to open-source projects are a plus.
 
 
 </li>
 </ul>
 If you are a passionate and experienced Data Scientist / Senior Machine Learning Engineer who thrives on solving challenging problems and wants to make a significant impact in the field, we encourage you to apply and join our dynamic team at Arrow Electronics Together, we will drive innovation and shape the future of data science and machine learning.
 
 
 
 Location:
 
 
 
 EG-Cairo, Egypt (Al Emdad &amp; Al Tamween)
 
 
 
 Time Type
 
 
 
 Full time
 
 
 
 Job Category
 
 
 
 Information Technology
</div>

Data Scientist / Senior Machine Learning Engineer

Capital One

<div>Lead Machine Learning Engineer, Card Tech
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.
What You&rsquo;ll Do:
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:<ul><li>
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</li><li>
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)</li><li>
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment</li><li>
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</li><li>
Retrain, maintain, and monitor models in production</li><li>
Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>
Construct optimized data pipelines to feed ML models</li><li>
Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code</li><li>
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</li><li>
Use programming languages like Python, Scala, or Java</li></ul>
Basic Qualifications:<ul><li>
Bachelor&rsquo;s Degree</li><li>
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</li><li>
At least 4 years of experience programming with Python, Scala, or Java</li><li>
At least 2 years of experience building, scaling, and optimizing ML systems</li></ul>
Preferred Qualifications:<ul><li>
Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field</li><li>
3+ years of experience building production-ready data pipelines that feed ML models</li><li>
3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</li><li>
2+ years of experience developing performant, resilient, and maintainable code</li><li>
2+ years of experience with data gathering and preparation for ML models</li><li>
2+ years of people leader experience</li><li>
1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation</li><li>
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>
Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</li><li>
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</li><li>
Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion</li></ul>
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
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.
New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer 
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.
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.
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.
This role is expected to accept applications for a minimum of 5 business days.
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.
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.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
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.
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).</div>

Lead Machine Learning Engineer, Card Tech

JPMorganChase

<div>JOB DESCRIPTION 
 
 
Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction. In this role, you'll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You'll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows.
As an Applied AI Engineer-Senior Associate within the Consumer &amp; Community Banking Machine Learning team, you will work with agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job Responsibilities
<ul><li>Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors</li>
<li>Develops secure and high-quality production code, and reviews and debugs code written by others</li>
<li>Drives decisions that influence the product design, application functionality, and technical operations and processes</li>
<li>Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle</li>
<li>Influences peers and project decision-makers to consider the use and application of leading-edge technologies</li>
<li>Drive innovation in machine learning solutions, ensuring scalability, flexibility, and future-proof architecture</li>
<li>Act as a thought leader and trusted advisor to executive leadership, providing strategic recommendations on machine learning initiatives and solutions</li>
<li>Architect and oversee the development of next-generation machine learning models and systems leveraging cutting-edge technologies</li>
<li>Ensure the platform supports complex use cases, including real-time predictions, big data processing, and advanced analytics</li>
<li>Promote software and model quality, integrity, and security across the organization
</li></ul>Required qualifications, capabilities, and skills
<ul><li>BS in Computer science or similar fields with 5+ years experience or MS in Computer science or similar fields with 3+ years experience, with training and work experience in LLM/NLP and search.
</li><li>Proven track record of building and scaling software and or machine learning platforms in high-growth or enterprise environments</li>
<li>Experience in machine learning frameworks, ML Ops tools and practices.
</li><li>Strong proficiency in engineering programming languages (e.g., Python, Java) and infrastructure as code (e.g., Terraform, CloudFormation)</li>
<li>Proficient in building AI Agents (e.g., LangChain, LangGraph, AutoGen), integration of tools (e.g., API) and RAG based solutions (e.g., open search)
</li><li>Hands-on experience with software development pipeline and orchestration tools (e.g., Jenkins, GitLab CI/CD)
</li></ul>Preferred qualifications, capabilities, and skills
<ul><li>Optional, great to have - experience in developing large-scale machine learning solutions based on big data to solve real world problems (e.g. Classification, Regression, or Recommender Systems).
</li></ul>ABOUT US 
 
 
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Equal Opportunity Employer/Disability/Veterans 
 
 
ABOUT THE TEAM 
 
 
<div>Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction. 
</div><div></div> 
<div>The CCB Data &amp; Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.</div></div>

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