Staff AI Engineer

Capital One is building responsible, reliable AI systems and foundational model platforms. This remote-eligible Staff AI Engineer role partners with engineers and research teams to design, develop, deploy, and support AI software components and enterprise AI architecture standards that help associates work more effectively and enable better customer experiences.

What you’ll do

• Collaborate with cross-functional teams including engineers, research scientists, technical program managers, and product managers to deliver AI-powered products.
• 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.
• Build with a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
• Introduce state-of-the-art foundation model optimization techniques to improve performance across scalability, cost, latency, and throughput for large-scale production systems.
• Contribute to the technical vision and long term roadmap for foundational AI systems.
• Set enterprise-wide AI architecture direction by unifying tooling, observability, and deployment standards across teams.
• Own design and integration for model routing, caching, and orchestration systems supporting hybrid and multi-model workloads.
• Champion responsible AI principles across system design with transparency, reproducibility, and fairness-by-design.
• Drive internal education, mentorship, and best-practice dissemination through architecture councils and AI guilds.

Requirements

• Bachelor’s degree in Computer Science/AI/Electrical Engineering/Computer Engineering (or related fields) plus 8+ years experience developing AI/ML algorithms or technologies, or Master’s degree plus 6+ years experience.
• 8+ years programming experience with Python, Go, Scala, CUDA, or Java.
• Ability to stay current with research and apply novel techniques judiciously in production.
• Comfort adapting to big, undefined problems and bringing clarity.
• Strong questioning and deep problem-solving skills, with the ability to articulate findings clearly.
• Willingness to share 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 a trailblazing mindset to forge new paths toward business goals when the route is unclear.

Technologies

• AWS Ultraclusters, Huggingface, VectorDBs, PyTorch
• Python, Go, Scala, CUDA, Java
• AWS, Google Cloud, Azure
• C++, C#, Golang, C++

Salary and incentives

Staff AI Engineer salary is USD 244,700 - 279,200 per year for Remote (regardless of location). Performance-based incentive compensation may be available, potentially including cash bonus(es) and/or long term incentives (LTI).

• Remote (Regardless of Location): $244,700 - $279,200
• McLean, VA: $269,100 - $307,200
• Cambridge, MA: $269,100 - $307,200
• New York, NY: $293,600 - $335,100
• San Francisco, CA: $293,600 - $335,100
• San Jose, CA: $293,600 - $335,100

Benefits

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

Preferred qualifications

• Experience designing AI systems with tradeoffs around cost, latency, throughput, and accuracy.
• 8+ years 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.
• Proven ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level.
• Experience developing AI/ML algorithms such as LLM inference, similarity search and VectorDBs, guardrails, and 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.
• Track record 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.
• Experience influencing research-to-production promotion processes, including model handoff, evaluation, and productization.
• Experience defining north-star metrics balancing business value, innovation velocity, cost, and ethical responsibility.
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length and token inputs/outputs.