Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Machine Learning Engineer 4 - Manager IC role.

Rezi rewrites your resume against Capital One's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Machine Learning Engineer 4 - Manager IC posting at Capital One — free, in seconds.

About the Role

As a Machine Learning Engineer on the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One. 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.

Responsibilities

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 ML infrastructure decisions using 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

Requirements

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
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

Skills

Python
Java
Golang
C++
PyTorch
Tensorflow
Pandas
NumPy
Scikit-learn
Spark
Ray
AWS
GCP
Azure
Kubernetes
Docker
Kubeflow
CI/CD
Transformers
CNNs
RNNs
Supervised learning
Unsupervised learning
Reinforcement learning

Location

McLean, VA
New York, NY
San Jose, CA

Work Type

Full-time

Experience Level

4+ years programming experience
4+ years Machine Learning experience
4+ years distributed systems experience
2+ years production ML deployment experience
2+ years cloud services experience
3+ years ML model design, training, optimization, and evaluation
3+ years architecting and scaling distributed ML systems
3+ years managing ML workloads with Docker, Kubernetes, and pipeline orchestration tools
3+ years applying modern software best practices
1+ years technical lead experience

Education Level

Bachelor's Degree in Computer Science, Machine Learning or a related quantitative field
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field

Salary/Compensations

McLean, VA: $197,300 - $225,100
New York, NY: $215,200 - $245,600
San Jose, CA: $215,200 - $245,600

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
Eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

About the Company

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.
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).

Equal Opportunity

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.
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.