Machine Learning Engineer 4 - Manager IC
Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One - ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads. In this role, you will integrate agentic AI into modern engineering lifecycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security.
What You’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:
- 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
- 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)
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- 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
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- 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
- Use programming languages like Python, Scala, or Java
Basic Qualifications:
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 4 years of experience programming with Python, Java, Golang, or C++
- At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
- At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data
- At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems
Preferred Qualifications:
- Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
- 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging.
- 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization.
- 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus).
- 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning.
- 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation
- Authored/co-authored a paper on a ML technique, model, or proof of concept
At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any 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.
McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4
New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4
San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4
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’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’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
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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).