machine learning engineer

Quantcast

<div><div>At Quantcast, we don't just build advertising technology, we revolutionize how it works. Our AI-powered Demand Side Platform (DSP) connects the world's most ambitious marketers with their ideal audiences across the open internet, delivering results that actually move the needle. Since 2006, we've been the industry's trailblazer, launching the first AI-powered measurement platform for publishers and the first AI-driven DSP. Our AI doesn't just optimize&mdash;it delivers the measurable outcomes that matter most to our clients, giving them the competitive edge they need in a crowded marketplace. Ready to join the team that's defining the future of digital advertising?</div><div></div><div> 
The Modeling team is responsible for Machine Learning (ML) systems at Quantcast. We build and maintain multiple ML products that price millions of bid requests per second in a real-time auction environment to maximize advertiser outcomes. For each bid our models predict age, gender, viewability, fraud, advertiser relevance and many more characteristics. Using NLP, clustering and LLMs we build topics in multiple languages to help our advertisers target customers interested in relevant content.</div>
<div></div>
<div>As a Machine Learning Engineer you care about the health and maintainability of our systems and the velocity of the engineering teams. You explore data, research new algorithms, experiment with proof of concepts, and build out scalable real-time production systems to tackle challenges the company faces.</div>
<div></div>
<h3 class="jobSectionHeader">What you'll do:</h3><ul>
<li>Design, code, test, and debug ML applications and constantly improve large-scale global systems that respond to millions of real-time requests per second efficiently.</li>
<li>Run Machine Learning experiments to test new modeling ideas.</li>
<li>Write clean, efficient, and maintainable code using industry best practices.</li>
<li>Collaborate closely with engineering teams, to deliver high-quality ML products. Participate in code reviews and provide constructive feedback to team members.</li>
<li>Identify performance bottlenecks and optimize system components for enhanced scalability.</li>
<li>Keep up to date with developments in machine learning outside the company.</li>
</ul><h3 class="jobSectionHeader">Who you are:</h3><ul>
<li>New Grad to 1 year of experience.</li>
<li>You must be work-authorized in the United States without the need for employer sponsorship.</li>
<li>This is a hybrid role based in our San Francisco office. To ensure a manageable commute for in-office days, candidates must reside within a 60-mile radius of San Francisco, CA. No relocation candidates at this time.</li>
<li>Fluency in Python, Java or similar programming languages.</li>
<li>Strong skills in mathematics and statistics.</li>
<li>Working knowledge of ML algorithms such as classification, control systems, optimization, clustering, LLMs or recommendation systems.</li>
<li>An interest in distributed system and software design, concurrent algorithms, data structures, and software engineering.</li>
</ul><div>The expected base salary range for this role is $151,300 to $178,000.</div><div></div><div> 
At Quantcast, we craft offers that reflect your unique skills, expertise, and geographic location. On top of a competitive salary, this position includes a performance bonus, equity, and a comprehensive benefits package. For more details, visit our Careers Page and see how we support our team. We are headquartered in San Francisco with offices around the world. Quantcast is an Equal Opportunity Employer. Please see the Applicant Privacy Notice for details on our applicant privacy policy. Join the team that unlocks potential.</div></div><div></div>

Machine Learning Engineer

Salesforce

<div>To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.
Job Category
Software Engineering
Job Details
About Salesforce
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn&rsquo;t a buzzword &mdash; it&rsquo;s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level-up your career at the company leading workforce transformation in the agentic era? You&rsquo;re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Lead member of technical staff - Machine Learning Engineering
Job Category: Software Engineering
Job Details
About Salesforce
We&rsquo;re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too &mdash; driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good &ndash; you&rsquo;ve come to the right place.
We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.
We are looking for a highly motivated, hands-on lead machine learning engineer with a strong business understanding of cybersecurity problems, who acts as a force multiplier security data scientist for our security organization. The lead will not simply build models; they will architect the data-driven strategy for our threat detection capabilities.
Your impact:
<ul><li>Shape the Defense Strategy: You will own the decision-making process&mdash;translating vague security threats into concrete mathematical problems. By championing a rapid prototyping culture, you will validate hypotheses in days rather than months, ensuring our engineering resources are focused only on high-value detections while killing low-signal ideas early.
</li><li>Detect the &quot;Unknown Unknowns&quot;: You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learing. Your work will expose sophisticated threats&mdash;such as active system intrusions, lateral movement, beaconing, and insider attacks&mdash;that evade traditional defenses, directly reducing the organization's risk surface.
</li><li>Elevate the Organization: You will act as a force multiplier, mentoring junior scientists and engineers, and building the internal tooling, feature stores, and libraries that make the whole team faster. You will influence the broader security engineering roadmap to ensure a closed loop security telemetry that is treated as a first-class citizen.
</li><li>Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts&mdash;minimizing &quot;alert fatigue&quot; and maximizing analyst efficiency.
</li></ul>Required skills:
<ul><li>Extensive experience (3-5+ years) in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing and deploying systems of anomaly detection, clustering, and graph models in production.
</li><li>Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
</li><li>Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
</li><li>Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
</li><li>Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
</li><li>Solid foundation in feature engineering techniques and the implementation of feature stores.
</li><li>Experience in formulating ML governance policies and ensuring adherence to data security regulations.
</li><li>Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership.
</li><li>Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations.
</li><li>High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap.
</li></ul>Preferred skills:
<ul><li>Masters or PhD in a quantitative field
</li><li>Expertise in advanced Natural Language Processing (NLP) methodologies.
</li><li>Experience contributing to open-source security data science tools.
</li><li>Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences.
</li><li>Background in offensive security (Penetration Testing/Red Teaming) with an &quot;attacker's mindset.&quot;
</li><li>Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
</li><li>Previous experience in a mentoring role for junior engineers.
</li><li>Track record of publications and/or patents in quantitative disciplines.
</li></ul>Unleash Your Potential
When you join Salesforce, you&rsquo;ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best , and our AI agents accelerate your impact so you can do your best . Together, we&rsquo;ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future &mdash; but to redefine what&rsquo;s possible &mdash; for yourself, for AI, and the world.
Accommodations
If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .
Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates&rsquo; resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.
Posting Statement
Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that&rsquo;s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications &ndash; without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.
In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions.&amp;#xa;&amp;#xa;The typical base salary range for this position is $172,500 - $260,100 annually. In select cities within the San Francisco and New York City metropolitan area, the base salary range for this role is $207,800 - $285,800 annually.&amp;#xa;&amp;#xa;The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.</div>

Lead Machine Learning Engineer - LMTS

Plaid

<div><div><h2 class="jobSectionHeader">Location</h2>
San Francisco HQ; New York City Office; Seattle Office
</div><div><h2 class="jobSectionHeader">Address</h2>
1098 Harrison Street, San Francisco, California, 94103
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Location Type</h2>
Hybrid
</div><div><h2 class="jobSectionHeader">Department</h2>
All DepartmentsData
</div><div><h2 class="jobSectionHeader">Compensation</h2>
<ul><li>$211,680 &ndash; $272,160</li>
</ul><div>Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.</div></div></div>
 <div><div>We believe that the way people interact with their finances will drastically improve in the next few years. We&rsquo;re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid&rsquo;s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
The Data Foundation &amp; AI team sits within Plaid&rsquo;s Data organization and is responsible for building the shared machine learning and AI infrastructure that powers innovation across Plaid&rsquo;s product portfolio. The team transforms Plaid&rsquo;s unique financial network data into scalable, general-purpose representations that can be leveraged by teams throughout the company. Working across the full model lifecycle, the team develops and curates pretraining datasets, trains and evaluates foundation models, and operates production-grade serving and monitoring systems to ensure reliable, high-impact AI capabilities at scale.
As a Research Scientist, you will advance Plaid&rsquo;s foundation models by developing novel model architectures, pretraining objectives, and fine-tuning strategies that generalize across a broad range of financial and product applications. You will contribute to the design, development, and operation of production machine learning systems, working across the full stack from data and feature engineering to training pipelines, model serving, and monitoring. The role also involves creating comprehensive evaluation frameworks that assess model performance across diverse tasks and use cases, ensuring robust and reliable outcomes. In close partnership with product and engineering teams, you will adapt foundation models to solve specific business challenges, validate their impact through rigorous experimentation, and translate research advances into production capabilities. You will also share insights and results with both internal and external audiences, helping drive innovation and elevate the practice of machine learning and AI across Plaid.
Responsibilities:
<ul><li>Building a foundation model on one of the world&rsquo;s richest financial datasets that no one else has.
</li><li>Doing research that ships: moving from experimentation and prototypes to production systems serving real customers.
</li><li>Working across the full ML stack, from pretraining objectives and architectures to serving infrastructure and monitoring.
</li><li>Collaborating with a high-caliber team and seeing your work amplify the capabilities of multiple product teams.
</li><li>Helping hundreds of millions of consumers achieve greater financial freedom through data-driven products.
</li></ul>Qualifications:
<ul><li>MS or PhD in ML/AI/CS/Stats/Applied Math (or closely related). PhD preferred but not required &mdash; candidates with equivalent industry research and production experience will be considered.
</li></ul><ul><li>1-3 years of industry experience building and deploying ML models, with evidence of both research depth and production delivery
</li><li>Strong applied ML research skills with production delivery experience
</li><li>Depth in Transformers/LLMs, representation learning, or large-scale model training
</li><li>Distributed training experience and strong Python + software engineering fundamentals
</li><li>Fintech / financial data domain experience - Nice to have
</li><li>Demonstrated ability to ship models to production (not just prototype) - Nice to have
</li><li>External publications or open-source contributions - Nice to have
</li></ul>Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com. 

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.
Compensation Range: $211,680 - $272,160</div></div>

Machine Learning Engineer (Research Scientist) - DFAI

Hayden AI

<div>
 
 About Us
 
 
 
 At Hayden AI, we are on a mission to harness the power of computer vision to transform the way transit systems and other government agencies address real-world challenges.
 
 
 From bus lane and bus stop enforcement to transportation optimization technologies and beyond, our innovative mobile perception system empowers our clients to accelerate transit, enhance street safety, and drive toward a sustainable future.
 
 
 
 About The Deep Learning Team
 
 
 
 The Deep learning team&rsquo;s work is at the crux of Hayden AI&rsquo;s solutions to its customers. The team is responsible for building and maintaining advanced models that are able to perceive the world and the knowledge produced by the models is used downstream to implement various customer use-cases. The team runs a number of models both in the cloud as well as on the edge device serving different scenarios. The team also works closely with the platform team to build out Hayden AI&rsquo;s MLOps infrastructure to achieve better scale and reliability.
 
 
 
 About The Role
 
 
 
 As a Deep Learning Engineer at Hayden, you will make key contributions towards building foundational AI capabilities for Hayden that will enable solving complex problems for Hayden AI Customers. In this role, you will build, train, evaluate and deploy state of the art ML models in production at scale. You will report into the Director of AI within the Perception Org and work closely with other engineering as well as product teams to deliver value to Hayden AI customers. We are looking for someone who has familiarity with or growing expertise in at least one of the verticals below &mdash; depth in a specific area is valued but not required on day one.
 
 
 <ul>
 <li>
 3D vision models to predict depth and 3D structure,
 </li>
 <li>
 Video/temporal behavior models to predict intent,
 </li>
 <li>
 Deep understanding of Vision language models and hands-on experience fine tuning them,
 </li>
 <li>
 Deep understanding of foundation models in perception.
 </li>
 <li>
 Deep knowledge of Nvidia edge device stack for running ML models and cuda know-how in terms delivering highly optimized models
 
 
 </li>
 </ul>
 This position is based in San Francisco and follows a hybrid schedule with at least 3 days in-office per week.
 
 
 
 
 Key Responsibilities
 
 
 
 
 Below are your primary responsibilities. These represent the core areas where you&rsquo;ll make an impact. As part of a rapidly evolving team, we look forward to your impact expanding over time.
 
 
 <ul>
 <li>
 Come up with solutions to complex problems in perception using deep learning.
 </li>
 <li>
 Articulate ideas both verbally and in written form culminating in clear design and project plan docs for their work
 </li>
 <li>
 Contribute to team roadmap and planning
 </li>
 <li>
 Work with cross functional teams across the company and contribute towards delivery of end to end solutions for the customer
 
 
 </li>
 </ul>
 
 Required Qualifications
 
 
 
 The qualifications below outline the experience and skills most relevant to success in this role. We recognize that skills and potential come in many forms, and we welcome diverse experiences that advance our mission.
 
 
 <ul>
 <li>
 Experience: 1-2 years experience building and deploying machine learning models for perception in production settings.
 </li>
 <li>
 Core Skills: Solid hands-on experience designing, training, and evaluating machine learning models for perception. Demonstrated ability to independently build ML pipelines and deploy models to cloud environments (AWS, GCP, or Azure); familiarity with MLOps practices including experiment tracking, model versioning, and automated workflows. Hands-on experience in PyTorch, Python, and related skills.
 </li>
 <li>
 Personal Attributes:Good communicator, self starter and ability to collaborate with others, quick learner who can adapt to a fast paced startup culture
 </li>
 <li>
 Education: Bachelors or Masters in Computer Science or related field
 </li>
 <li>
 Nice to Have: Experience working in perception problems in self driving car companies that aligns closely with the work of this team.
 
 
 </li>
 </ul>
 Compensation Range: $161,637 - $175,000
 
 
</div>

Deep Learning Engineer

Common Sense Media

<div>Data Scientist<div></div><div><div><div><div>
Organization 
Common Sense Media</div><div> 
Department 
Data</div><div> 
Reports To 
Senior Director, Data &amp; Analytics</div><div> 
Location 
San Francisco, CA</div><div><div> 
Classification 
Salary Range</div><div> 
Full-time, Exempt
$140,00&ndash;$166,250</div></div> 
</div></div></div>
POSITION OVERVIEW
Common Sense Media is seeking a Data Scientist to help build data capabilities that allows the organization to answer the questions that matter most to advancing our mission and driving impact for kids and families. This is a hands-on, full-stack individual contributor role responsible for developing pipelines, models, and analyses that turn data from across the Common Sense Media ecosystem into reliable, decision-ready insights.
The Data Scientist will develop predictive and statistical models that support organization-wide decisions, design and analyze experiments, apply causal inference methods, and lead impact analyses that measure how Common Sense Media's work affects kids, families, and educators, translating findings into clear, actionable recommendations for both technical and non-technical audiences. The Data Scientist's work will span the full range of data science at Common Sense Media, from foundational data engineering and data modeling through analytics, machine learning, experimentation, and impact analysis.
As a senior technical voice on the team, the Data Scientist will help uplevel the organization's capabilities in data science, machine learning, and generative AI, advising on methodology, helping to develop and test AI product features, and raising the technical bar within product and engineering.
KEY RESPONSIBILITIES
Analytics &amp; Modeling<ul><li>
Conduct rigorous exploratory and descriptive analyses that establish the foundational understanding of our audiences, programs, and performance.</li><li>
Develop predictive and statistical models that support organization-wide decisions.</li><li>
Apply modern machine learning techniques to ship models and analyses that drive impact.</li></ul>
Experimentation &amp; Causal Inference<ul><li>
Design and execute A/B tests and randomized controlled trials.</li><li>
Leverage rigorous quasi-experimental designs (e.g., difference-in-differences, regression discontinuity) and advanced causal inference techniques to evaluate the impact of complex product and program decisions where randomized testing is not feasible.</li></ul>
Impact Measurement<ul><li>
Lead rigorous impact analyses and program evaluations that measure how Common Sense Media's work affects kids, families, and educators.</li><li>
Apply causal inference and evaluation methods to measure real-world outcomes.</li></ul>
Insights &amp; Communication<ul><li>
Translate findings into clear, actionable recommendations for both technical and non-technical audiences.</li><li><h1 class="jobSectionHeader">
Partner closely with colleagues across Common Sense Media to put data to work across the organization.</h1></li></ul> 
Analytics Engineering<ul><li>
Own the data models that turn raw data into clean, reliable, analysis-ready data sets used across the organization.</li><li>
Work with internal teams and external partners to establish data collection approaches, integrate new data sources, and ensure that incoming data is structured and trustworthy.</li><li><h1 class="jobSectionHeader">
Build and maintain the data pipelines and transformations necessary to support modeling, reporting, and impact measurement.</h1></li></ul> 
Technical Leadership<ul><li>
Serve as a senior technical voice on the team, advising on methodology and raising the technical bar around how to use data in thoughtful decision-making.</li><li>
Help uplevel the organization's capabilities in data science, machine learning, and generative AI, supporting the development and testing of AI tools and features across the organization.</li></ul>
QUALIFICATIONS<ul><li>
8+ years of experience in applied data science, analytics, or a closely related quantitative field, with a track record of shipping models and analyses that have changed decisions.</li><li>
Strong hands-on skills in SQL and Python or R, and expert use of standard machine learning libraries.</li><li>
Experience building and maintaining production data pipelines using modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and modern data stack tooling (dbt, Airflow, Fivetran, or equivalents).</li><li>
Demonstrated experience designing and analyzing experiments, including A/B tests, quasi-experiments, and causal inference methods.</li><li>
Experience leading rigorous impact or program evaluation that measures real-world outcomes.</li><li>
Experience mentoring junior data scientists.</li><li>
Excellent written and verbal communication skills, with the ability to translate technical work into clear narratives for both technical and non-technical audiences.</li><li>
Demonstrated ability to serve as a senior technical voice within an organization, raising the technical bar of teammates and helping non-technical colleagues use data and AI tools effectively in their work.</li><li><h1 class="jobSectionHeader">
Strong understanding of data governance, privacy, and responsible data and AI practices, with particular sensitivity to data involving children and families.</h1></li><li><h1 class="jobSectionHeader">
Familiarity with product analytics, CRM, and marketing platforms (Amplitude, Google Analytics, Salesforce, Braze) and their underlying data structures is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience building predictive models for subscription, retention, lifetime value, or similar member or customer lifecycle problems is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience implementing and evaluating generative AI or LLM-based solutions, including prompt engineering, fine-tuning, retrieval-augmented generation, or self-hosted models, with rigorous evaluation against human or ground-truth baselines, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience designing AI evaluation frameworks, including human-in-the-loop evaluation, bias and fairness assessment, and ongoing monitoring of model performance, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Prior experience in a mission-driven, nonprofit, education, or media organization is a plus.</h1></li></ul>
WHAT WE OFFER<ul><li>
The opportunity to really make a difference in the lives of kids and families!</li><li>
A mission-driven team with a 23-year track record of impact for families and children.</li><li>
Competitive nonprofit compensation and comprehensive benefits.</li><li>
A collaborative, flexible work environment with meaningful leadership access and visibility.</li></ul>
ABOUT COMMON SENSE MEDIA
Common Sense Media is the leading nonprofit organization dedicated to improving the lives of kids and families by providing the research-backed information, education, and independent voice they need to thrive in the age of apps, algorithms, and AI. We rate, educate, and advocate for policies to protect and prepare kids online. Our ratings, research, and resources reach more than 150 million users globally, over 1.4 million educators, and more than 100,000 schools worldwide every year. Learn more at commonsense.org.</div>

Data Scientist

Information Technology Senior Management Forum

<div><div>Posted Date
6/29/2026
</div><div>Description
<div>Senior Lead AI Engineer(MLX, Agentic AI, Gen AI platform Services)
Overview:
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.
Team Description:
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. 
In this role, you will:<ul><li>
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.</li><li>
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.</li><li>
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.</li><li>
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.</li><li>
Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.</li></ul>
The Ideal Candidate:<ul><li>
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.</li><li>
Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.</li><li>
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.</li><li>
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.</li><li>
You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.</li></ul>
Basic Qualifications:<ul><li>
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</li><li>
At least 6 years of experience programming with Python, Go, Scala, or Java</li></ul>
Preferred Qualifications:<ul><li>
7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)</li><li>
Experience designing, developing, integrating, delivering, and supporting complex AI systems</li><li>
Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders</li><li>
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</li><li>
Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost</li><li>
Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production</li><li>
Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers</li></ul>
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
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.
McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer 

Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer 

New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer 

San Francisco, CA: $250,800 - $286,200 for Sr. Lead AI Engineer 

San Jose, CA: $250,800 - $286,200 for Sr. Lead AI 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).
Senior Lead AI Engineer(MLX, Agentic AI, Gen AI platform Services)
Overview:
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.
Team Description:
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. 
In this role, you will:<ul><li>
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.</li><li>
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.</li><li>
Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.</li><li>
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.</li><li>
Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.</li></ul>
The Ideal Candidate:<ul><li>
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.</li><li>
Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production.</li><li>
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.</li><li>
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.</li><li>
You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown.</li></ul>
Basic Qualifications:<ul><li>
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</li><li>
At least 6 years of experience programming with Python, Go, Scala, or Java</li></ul>
Preferred Qualifications:<ul><li>
7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)</li><li>
Experience designing, developing, integrating, delivering, and supporting complex AI systems</li><li>
Demonstrated ability to lead and mentor an engineering team and influence cross-functional stakeholders</li><li>
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</li><li>
Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost</li><li>
Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production</li><li>
Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers</li></ul>
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
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.
McLean, VA: $229,900 - $262,400 for Sr. Lead AI Engineer 

Cambridge, MA: $229,900 - $262,400 for Sr. Lead AI Engineer 

New York, NY: $250,800 - $286,200 for Sr. Lead AI Engineer 

San Francisco, CA: $250,800 - $286,200 for Sr. Lead AI Engineer 

San Jose, CA: $250,800 - $286,200 for Sr. Lead AI 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>
</div><div>Salary
229,900.00 - 262,400.00 Annual
</div><div></div><div><div>Type<div>
Full-time</div></div></div></div>

Senior Lead AI Engineer(MLX, Agentic AI, Gen AI platform Services)

REVI

We're hiring a Junior AI Engineer to help us design, build, and ship agentic AI systems and ML-powered features into production. You'll work alongside senior engineers on real problems &mdash; building agents that take actions on behalf of restaurants, tuning models that personalize customer experiences, and integrating frontier LLMs into our product stack. This is a hands-on builder role; expect to write production code, deploy services, and iterate quickly based on user feedback.What You'll DoBuild and ship agentic AI features &mdash; single-agent and multi-agent systems that perform real tasks for restaurants and their customersDevelop deterministic ML models (classification, ranking, regression, retrieval) for use cases across the platformIntegrate LLM APIs (Anthropic Claude, OpenAI GPT, and others) into product workflows, including prompt engineering, tool use, structured outputs, and retrieval-augmented generationDeploy and maintain AI/ML services in production &mdash; containerized, observable, and reliableWork closely with senior engineers and product to translate fuzzy product ideas into shipped featuresContribute to evaluation and monitoring frameworks so we know our agents and models actually workWhat We're Looking ForBasic Qualifications0&ndash;3 years of experience building AI/ML systems. This can come from full-time work, internships, or substantive class/research projects &mdash; what matters is depth, not the sourceDemonstrable hands-on experience with agentic AI &mdash; single-agent or multi-agent systems built using frameworks like LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom orchestrationWorking knowledge of deterministic ML modeling &mdash; building, training, and evaluating models with libraries like scikit-learn, XGBoost, LightGBM, or PyTorchHands-on experience integrating LLM APIs (Claude, GPT, Gemini, etc.) into a working application &mdash; including prompt design, tool calling, and handling streaming/structured responsesSome deployment experience &mdash; you've put a service behind an API, containerized it, and run it somewhere real (AWS/GCP/Azure, Modal, Render, Fly, etc.)Strong Python skills; comfort with Git, REST APIs, and basic SQLBS or MS in Computer Science, Machine Learning, or a related quantitative field &mdash; or equivalent demonstrable skillPreferred QualificationsExperience with vector databases (Pinecone, Weaviate, pgvector, etc.) and RAG pipelinesFamiliarity with LLM observability/eval tools (LangSmith, Langfuse, Braintrust)Exposure to distributed data processing (Spark, Databricks) or streaming systems (Kafka, Kinesis, Pub/Sub) &mdash; even from an internship or courseworkExposure to voice AI, speech-to-text, or real-time conversational systemsExperience with restaurant tech, commerce platforms, or B2B SaaSOur ValuesHeart: A team that is passionate about what they do, with a heart of giving back.Impact: Being a versatile team player with an innovative mind and a firm backbone to make an impact on everything they touch.Excellence: A team committed to excellence in all we do, with integrity and supreme service.Perks and Benefits of Joining the Revi TeamShip real agentic AI to real customers &mdash; not demosWork directly with senior engineers and leadership on architecture and product decisionsMove fast, own outcomes, and grow rapidly into a senior ICCompetitive compensation, meaningful early-stage equity, and a strong AI-forward cultureExcellent and comprehensive health plans (Medical, dental, vision, etc)Flexible Vacation Policy, Paid holidaysOrganized volunteer events to give back to our communityOff-sites, events and happy hours401kComp range - Base : 130k USD - 170k USD + EquityPay: $130,000.00 - $170,000.00 per yearWork Location: In person

JR. AI ENGINEER

Explore 90+ Machine learning engineer in San Francisco this July. Find roles across top companies and apply now to boost your career.