Capital One
AI Engineer 4 (MLX, Agentic AI, Gen AI workflows, GraphRAG)
$197K–$246K
New York, NY +4 · Full-time · Posted Oct 27, 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 AI solutions on cloud platforms.
- 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 AI systems and products, including foundation models, LLM inference, agents, and optimization for production performance and reliability.
What you'll do
- Design, develop, test, deploy, and support AI components and workflows, including model inference, agents, search, guardrails, evaluation, and observability.
- Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization; own architecture and reliability objectives.
- Partner across teams on AI product delivery, technical roadmaps, deployment reviews, governance, and mentoring.
What they ask for
- Bachelor’s degree in a related field and at least 4 years developing AI/ML algorithms or technologies, or a Master’s degree in a related field and at least 2 years in that work.
- At least 4 years programming with Python, Go, Scala, CUDA, or Java.
Nice to have
- Experience leading AI system development and making cost, latency, throughput, and accuracy tradeoffs.
- 6+ years deploying scalable, responsible AI solutions on cloud platforms.
Pay
$197,300 - $225,100 USD base salary (McLean, VA and Cambridge, MA); $215,200 - $245,600 USD base salary (New York, NY, San Francisco, CA, and San Jose, CA)
Tools
AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, Python, Go, Scala, CUDA
AI focus
Develop production AI systems, including foundation model training and inference, agentic and multi-agent workflows, similarity search, guardrails, model evaluation, governance, and optimization.
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