[Remote] Staff AI Engineer role (Remote Eligible)

Note: The job is a remote job and is open to candidates in USA. Capital One is building responsible and reliable AI systems to transform banking and deliver personalized customer experiences. The Staff AI Engineer will design, develop, deploy, and support foundational AI systems, including model training, inference, agent workflows, optimization, governance, and observability. The role also sets enterprise AI architecture direction, advances responsible AI practices, and mentors engineering teams.

Responsibilities

• 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
• Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc
• Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more
• Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance — scalability, cost, latency, throughput — of large scale production AI systems
• Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One
• Set the technical direction for enterprise-wide AI architecture - unifying tooling, observability, and deployment standards across teams
• Own the design and integration of model routing, caching, and orchestration systems that support hybrid and multi-model workloads
• Champion responsible AI principles across all system design, embedding transparency, reproducibility, and fairness-by-design
• Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds

Skills

• Bachelor'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, or a Master'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
• At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java
• Experience designing AI systems with tradeoff decisions around cost, latency, throughput and accuracy
• 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud)
• Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems
• Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level
• Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang
• Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost
• Experience in building agentic AI systems and agentic workflows
• Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production
• Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers
• Proven track record of defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability and evaluation frameworks
• Experience leading federated or multi-cloud AI strategies to maximize resilience, compliance and compute efficiency
• Demonstrated success influencing research-to-production promotion processes - guiding model handoff, evaluation and productization
• Experience defining north-star metrics for AI systems that balance business value, innovation velocity, cost, and ethical responsibility
• Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs)

Benefits

• 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 will consider sponsoring a new qualified applicant for employment authorization for this position.
• Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
• Remote work eligibility

Company Overview

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