AI Engineer 5 (AI Foundations)

Capital One • San Jose, CA

4d • $229,900 - $286,200

About The Position

Capital One is hiring an AI Engineer 5 for its Intelligent Foundations and Experiences (IFX) team, which builds and deploys proprietary AI solutions and platforms for use across the company. The role partners with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.

The engineer designs, develops, tests, deploys, and supports foundational AI components and production systems, including foundation-model training, LLM inference, agents and multi-agent workflows, similarity search, guardrails, evaluation, experimentation, governance, and observability. The role applies open-source and SaaS technologies and develops optimization techniques to improve the scalability, cost, latency, and throughput of large-scale AI systems. It also contributes to the technical vision and roadmap for foundational AI, integrates different models and retrieval systems into orchestration pipelines, and leads governance reviews and design councils to promote cost-performance, technical consistency, and compliance with AI engineering standards.

The position calls for a technically deep, resilient engineer who keeps up with AI research, applies novel techniques judiciously in production, brings clarity to ambiguous problems, and communicates findings effectively. The role also mentors Principal- and Manager-level AI engineers and helps raise organizational technical maturity. Capital One will consider sponsoring a new qualified applicant for employment authorization. The role is eligible for performance-based incentive compensation. The stated full-time annual salary ranges vary by location: $229,900–$262,400 in Cambridge, MA and McLean, VA, and $250,800–$286,200 in New York, NY and San Jose, CA. The position is expected to accept applications for at least five business days.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field and at least 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of these fields and at least 4 years of such experience.
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.
• Strong engineering and mathematics foundation, with expertise in hardware, software, and AI.
• Ability to understand scientific publications and judiciously apply novel techniques in production.
• Ability to adapt quickly, bring clarity to undefined problems, investigate root causes, and communicate findings clearly.
• Resilience and ability to pursue business goals when the path is unknown.

Nice To Haves

• Experience leading AI systems development and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• At least 7 years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or an equivalent private cloud.
• Experience designing, developing, delivering, and supporting complex AI systems.
• Experience developing AI and ML algorithms or technologies, such as LLM inference, similarity search, VectorDBs, guardrails, or memory, using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art training and inference optimization techniques to improve hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Passion for keeping up with AI research and systems, and applying novel techniques judiciously in production.
• Excellent communication and presentation skills, including explaining complex AI concepts to peers.
• Experience architecting and integrating heterogeneous AI systems—including rule-based, retrieval-augmented, and generative components—into unified production pipelines.
• Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes.
• Demonstrated ability to balance model performance and operational cost using dynamic inference strategies and model compression.
• Experience right-sizing models, instance counts, and hardware types based on requirements such as context length, token inputs, and token outputs.

Responsibilities

• Partner with 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, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability.
• Use open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch.
• Develop state-of-the-art foundation-model optimization techniques to improve production AI systems’ scalability, cost, latency, and throughput.
• Contribute to the technical vision and long-term roadmap for foundational AI systems at Capital One.
• Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models.
• Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency.
• Lead team design councils or design review boards to promote technical consistency and compliance with AI engineering standards.
• Mentor Principal- and Manager-level AI engineers, foster cross-domain learning, and elevate organizational technical maturity.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior