Staff AI Engineer

Capital One is hiring a Staff AI Engineer (remote eligible) to help build responsible and reliable AI systems and foundational model platforms. You will work across engineering, research, and product to design, develop, deploy, and support AI software components, while shaping enterprise AI architecture standards.

What you’ll do

• Collaborate with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that improve how associates work and how 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.
• Apply and combine Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
• Introduce state-of-the-art foundation model optimization techniques that improve scalability, cost, latency, and throughput for production AI systems.
• Help define the technical vision and long-term roadmap for foundational AI systems at Capital One.
• Set enterprise-wide AI architecture direction by unifying tooling, observability, and deployment standards across teams.
• Own the design and integration of model routing, caching, and orchestration systems for hybrid and multi-model workloads.
• Champion responsible AI principles in system design, emphasizing transparency, reproducibility, and fairness-by-design.
• Drive internal education and mentorship through architecture councils and AI guilds, sharing best practices across the organization.

Requirements

• Bachelor’s degree in Computer Science/AI/Electrical Engineering/Computer Engineering or a related field plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master’s degree plus at least 6 years.
• At least 8 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Strong ability to read and understand scientific publications and apply novel techniques thoughtfully in production.
• Ability to bring clarity to large, undefined problems and communicate findings concisely.
• Comfort sharing and advocating for new ideas, even when unproven.
• Deep technical foundation in engineering and mathematics, with expertise spanning hardware, software, and AI to identify optimization opportunities.
• Resilience and initiative to forge new paths to achieve business goals when the route is unclear.

Preferred qualifications

• Experience designing AI systems with tradeoff decisions across cost, latency, throughput, and accuracy.
• 8+ years deploying scalable and responsible AI solutions on cloud platforms such as 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 (for example: LLM inference, similarity search and VectorDBs, guardrails, memory) using Python, C++, C#, Java, CUDA, or Golang.
• Experience optimizing training and inference software to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and agentic workflows.
• Excellent communication and presentation skills for articulating complex AI concepts to peers.
• Experience defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability, and evaluation frameworks.
• Experience leading federated or multi-cloud AI strategies for resilience, compliance, and compute efficiency.
• Experience influencing research-to-production promotion processes, including model handoff, evaluation, and productization.
• Experience defining north-star metrics that balance business value, innovation velocity, cost, and ethical responsibility.
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length, token inputs, and token outputs.

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits to support your total well-being.

Salary

Remote (regardless of location): $244,700 - $279,200 per year

McLean, VA: $269,100 - $307,200 per year

This role may also be eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).