Capital One — a technology-focused financial services company where makers, builders, and problem-solvers work together on meaningful customer and business needs — is seeking a Staff Machine Learning Engineer to advance its artificial intelligence capabilities and bring sophisticated machine learning solutions into production. In this senior technical role you will shape the design, development, and operation of intelligent systems, build data pipelines, optimize models, and deploy cloud-based services at scale. With a stated salary range of $244,700 to $307,200 depending on location, plus possible performance-based incentives, this is a high-impact opportunity for experienced ML engineers in financial services.
About This Role — Staff Machine Learning Engineer at Capital One
Company: Capital One — brings together product, engineering, architecture, and data science teams to develop innovative solutions, using machine learning and cloud technologies to change how financial services are designed and delivered
Role Level: Senior technical role — guides technical decisions, contributes to major ML initiatives, and helps evolve systems and practices across the model lifecycle
Core Work: Building data pipelines, developing and optimizing models, and deploying cloud-based services that operate at scale
Culture: Collaboration, inclusion, experimentation, and continuous improvement, with room to take on complex, large-scale technology challenges
Listing Note: This posting appears on LinkedIn under AgileGrid Solutions; the role and company details below are as described in the job text
Why This Staff ML Engineer Role Stands Out in the US Market
Top-Tier Compensation: A stated range of roughly $245K–$307K base salary, with possible cash bonuses and long-term incentives depending on the plan
Production Focus: The emphasis is on shipping machine learning into production — not research in isolation — at the scale of a major financial services company
Technical Leadership: You will guide technical decisions, share best practices, and help recruit, develop, and retain engineering talent
Sponsorship: Capital One will consider sponsorship for a new qualified applicant for employment authorization
Position Overview
The Staff Machine Learning Engineer will design, build, and deliver machine learning models and software components for complex financial services challenges, partnering with product, architecture, engineering, and data science colleagues to turn ideas into reliable, scalable systems. You will select appropriate algorithms, frameworks, and architectures; use cloud technologies to deploy and scale models; optimize data pipelines; improve the performance and reliability of production services; and promote sound practices across engineering, modeling, deployment, and operations — while communicating technical decisions clearly to varied audiences and supporting the growth of other engineers.
Why This Machine Learning Engineer Role Stands Out: You work on production machine learning inside a large, technology-driven financial services company — where models directly affect real customers and business outcomes, the combination of PyTorch or TensorFlow modeling, distributed data processing with Spark or Ray, and Kubernetes-based deployment gives you hands-on ownership of the whole model lifecycle rather than a single narrow stage, the staff-level scope — guiding technical decisions, mentoring engineers, and contributing to hiring — builds leadership credentials that open doors to principal and director-track roles, a stated base salary range of about $245K to $307K with possible bonuses and long-term incentives places this among the strongest compensation packages for ML engineers in the US, and a collaborative culture that values experimentation, inclusion, and continuous improvement means your ideas are expected, not just tolerated.
Key Responsibilities
Model Development & Delivery
- Design, build, and deliver machine learning models and software components for complex financial services challenges
- Develop and evolve intelligent systems, selecting appropriate algorithms, frameworks, and architectures for each problem
- Optimize algorithms and infrastructure so models perform reliably under real production load
- Apply programming languages such as Python, Scala, Java, or Go, and use distributed or accelerated computing such as Dask and RAPIDS where appropriate
Cloud Deployment & Production Operations
- Use cloud technologies to deploy and scale models and to run them as dependable production services
- Optimize data pipelines for model training and evaluation, improving performance and reliability
- Operate Kubernetes-managed containerized systems and production machine learning solutions
- Promote sound practices across engineering, modeling, deployment, and operations
Collaboration & Technical Leadership
- Collaborate with product, architecture, engineering, and data science partners to define solutions with customer needs in mind
- Lead large-scale initiatives and guide technical decisions across the model lifecycle
- Communicate technical decisions clearly to varied audiences, from fellow engineers to business stakeholders
- Contribute to a collaborative, inclusive team culture of experimentation and continuous improvement
Talent Development
- Share engineering best practices and mentor other engineers
- Help recruit, develop, and retain talented engineering professionals
- Support the growth of colleagues through code review, design guidance, and technical coaching
Qualifications & Requirements
Required
- Bachelor’s degree or higher in computer science, machine learning, or a related quantitative field such as statistics, economics, operations research, analytics, mathematics, or engineering
- At least 8 years of programming experience with Python, Java, Go, or C++
- At least 6 years of machine learning experience with PyTorch or TensorFlow, and libraries such as Pandas, NumPy, or scikit-learn
- At least 6 years working with distributed systems such as Spark or Ray to prepare machine learning data
- At least 5 years deploying and operating production machine learning solutions, cloud-based services, and Kubernetes-managed containerized systems
Preferred
- A graduate degree in a relevant field
- Experience architecting resilient, large-scale machine learning systems and optimizing algorithms and infrastructure
- Experience designing production data pipelines for model training and evaluation
- Familiarity with supervised, semi-supervised, unsupervised, or reinforcement learning; common model types and architectures; training methods; and model evaluation
- Strong communication skills and a record of technical leadership, mentoring, or contributions to the machine learning community
Application & Employment Notes
Please Read Before Applying
Applications are expected to be accepted for at least five business days. Actual salary is confirmed in the offer letter, and pay ranges differ for other work locations. No agencies, please. Applicants who need an accommodation during the application process may contact Capital One Recruiting; accommodation information is kept confidential. Capital One is an equal opportunity employer, maintains a drug-free workplace, and considers qualified applicants with criminal histories in line with applicable law.
About Capital One — Technology-Driven Financial Services
Capital One is a technology-focused financial services company where makers, builders, and problem-solvers work together to address meaningful customer and business needs. Product, engineering, architecture, and data science teams combine to develop and deliver innovative solutions, and machine learning and cloud technologies are central to how the company designs and delivers financial services.
The organization values collaboration, inclusion, experimentation, and continuous improvement, and offers engineers an environment in which to take on complex challenges and contribute to large-scale technology initiatives. A comprehensive benefits program supports health, financial security, and overall well-being, with eligibility varying by employment status and management level, and the role may qualify for performance-based incentive compensation.
Career Excellence: Bring your machine learning, distributed systems, and cloud engineering depth to a staff-level role where production impact, technical leadership, and top-of-market compensation come together.
Who Should Apply?
- Senior ML Engineers: With 6+ years in machine learning and 8+ years of programming who already ship models into production
- MLOps & Platform Engineers: With strong Kubernetes, containerization, and cloud deployment experience for ML services
- Distributed Data Specialists: Who prepare training data at scale with Spark, Ray, Dask, or RAPIDS
- Technical Leaders & Mentors: Who guide design decisions, coach engineers, and help build strong teams
- Fintech & Quantitative Professionals: With backgrounds in computer science, statistics, mathematics, or engineering and an interest in financial services
Recently Opening Job👇
