Capital One
AI Engineer 4 (AI Foundations: LLM Customization, Finetuning, Reinforcement Learning)
$197K–$246K
New York, NY +4 · Full-time · Posted Sep 18, 2026
- Location
- New York, NY
San Francisco, CA
McLean, VA
Cambridge, MA
San Jose, CA - Experience
- At least 4 years developing AI/ML algorithms or technologies with a Bachelor’s degree, or at least 2 years with a Master’s degree; at least 4 years programming. Preferred: 6 years deploying cloud AI solutions.
- Education
- Bachelor's or Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields.
- Visa sponsorship
- Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
About the job
Build and operate scalable foundational AI systems and products, including model training and inference, agent workflows, optimization, reliability, and governance.
What you'll do
- Design, develop, deploy, and support AI software components and systems.
- Develop optimization techniques for production foundation models and improve performance, cost, latency, throughput, and hardware utilization.
- Own AI system architecture and reliability practices, and lead cross-functional deployment reviews covering security, governance, and compliance.
What they ask for
- Bachelor’s degree in a related field plus at least 4 years developing AI/ML algorithms or technologies, or a Master’s degree in a related field plus at least 2 years of that experience.
- At least 4 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development with tradeoff decisions involving cost, latency, throughput, and accuracy.
- Experience deploying scalable, responsible AI solutions on cloud platforms.
Pay
$197,300 - $225,100 USD base salary (Cambridge, MA; McLean, VA); $215,200 - $245,600 USD base salary (New York, NY; San Francisco, CA; San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Foundation model training and optimization, LLM inference and customization, agents and multi-agent workflows, similarity search, guardrails, evaluation, governance, and observability.
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