Senior Staff Engineer, AI Compute (Remote Eligible)
About the role
Capital One is seeking a Senior Distinguished Engineer, a hands-on technical leader passionate about distributed systems, to engineer and scale foundational compute capabilities for our machine learning platform. You will use your experience in building large scale, highly available and high performance systems to develop our common compute infrastructure on top of CPU and GPU substrates. Your contributions will power everything from developer notebooks to ML/DL model training, model inference and feature generation pipelines to pre-training and fine tuning Transformer-based models as well as generative AI inference and agentic applications.
Team description
The Capital One machine learning platform organization manages our cloud-based enterprise AI+ML system delivering the high-scale developer and runtime environments required to build, orchestrate, and deploy compute and data intensive AI systems across real-time and batch workloads.
Responsibilities
- Architect and build control and data plane implementations required to realize a highly available, multi-tenant, large scale and a secure machine learning platform
- Develop Ray and Spark distributed compute engine solutions to accelerate diverse workloads from LLM pre-training and reinforcement learning to large-scale data processing, while maximizing compute unit economics
- Engineer systemic improvements for operational excellence including automating KTLO (Keep The Lights On) workflows
- Direct the technical execution of a diverse project portfolio, collaborating with developers specializing in everything ranging from distributed microservices to running large foundation models
- Work cross-functionally with product and program management disciplines, and stakeholder and partners across Capital One to help optimize business outcomes while driving towards strong technology solutions
- Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and leading system design and code review sessions
- Help elevate the Capital One Distinguished Engineering community and establish yourself as a go-to resource on given technologies and technology-enabled capabilities
- Lead the way in creating next-generation talent, mentoring internal talent and actively recruiting external talent to bolster the Capital One tech talent pool
Basic qualifications
- Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 10 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 8 years of experience developing AI and ML algorithms or technologies
- At least 10 years of experience programming with Python, Go, Scala, or Java
Preferred qualifications
- Master's Degree in Computer Science or a Master's Degree in Software Engineering
- Hands on experience in the internals of Ray (Actors/GCS/Scheduling) or Spark (Query Optimizer/Memory Management)
- Experience building platforms that support LLM training, fine-tuning, or high-throughput inference
- Hands-on experience with AWS-specific compute primitives (EKS, EC2 UltraClusters, Graviton) and cost-optimization strategies
- History of upstream contributions to major distributed systems projects
Pay
- Remote (Regardless of Location): $286,200 - $326,700
- Cambridge, MA: $314,800 - $359,300
- McLean, VA: $314,800 - $359,300
- New York, NY: $343,400 - $392,000
- Richmond, VA: $286,200 - $326,700
- San Francisco, CA: $343,400 - $392,000
- San Jose, CA: $343,400 - $392,000
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
Benefits
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.