AI Engineer 5 (AI Foundations)

Capital One • San Jose, CA

1d • $229,900 - $286,200

About The Position

The AI Engineer 5 will work on Capital One’s Intelligent Foundations and Experiences (IFX) team, partnering with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers. The team develops and deploys proprietary AI solutions and platforms intended to enhance products responsibly and at scale.

The role designs, develops, tests, deploys, and supports AI software components such as foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability. It uses open-source and SaaS AI technologies and develops foundation-model optimization techniques to improve scalability, cost, latency, and throughput in production systems.

The engineer contributes to the technical vision and long-term roadmap for foundational AI systems; builds and optimizes multi-model orchestration pipelines combining LLMs, vector search, and domain-specific models; and leads cost-performance governance reviews and technical design councils. The role also mentors Principal- and Manager-level AI engineers and helps raise organizational technical maturity. Capital One describes the ideal candidate as technically deep, curious about current research, adaptable in ambiguous situations, committed to high-quality work, and willing to pursue novel solutions responsibly.

Requirements

• Bachelor’s degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field, plus at least 6 years of experience developing AI and ML algorithms or technologies; or a master’s degree in one of those fields plus at least 4 years of that experience.
• At least 6 years of programming experience with Python, Go, Scala, CUDA, or Java.

Nice To Haves

• Experience leading development of AI systems and making tradeoff decisions involving cost, latency, throughput, and accuracy.
• 7 or more years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or 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 and vector databases, guardrails, or memory using Python, C++, C#, Java, CUDA, or Golang.
• Experience developing and applying state-of-the-art techniques to optimize training and inference software for hardware utilization, latency, throughput, and cost.
• Experience building agentic AI systems and workflows.
• Interest in current AI research and systems, with the ability to judiciously apply novel techniques in production.
• Excellent communication and presentation skills, including the ability to explain 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 for requirements such as context length and input and output token volumes.

Responsibilities

• Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
• 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, vector databases, and PyTorch.
• Develop foundation-model optimization techniques to improve production AI system 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, fostering cross-domain learning and organizational technical maturity.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by full- or part-time status, exempt or non-exempt status, and management level.
• Eligible for 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