Capital One
AI Engineer 4 (MLX, Agentic AI, Gen AI platform Services)
$197K–$246K
San Jose, CA +4 · Full-time · Posted Sep 18, 2026
- Location
- San Jose, CA
San Francisco, CA
McLean, VA
Cambridge, MA
New York, NY - Experience
- At least 4 years developing AI and 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 scalable, responsible 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 foundational AI systems and AI-powered products, optimizing models and inference for performance, cost, latency, and throughput.
What you'll do
- Design, develop, deploy, and support AI components such as foundation-model training, LLM inference, agents, similarity search, guardrails, evaluation, and observability.
- Optimize production AI systems and own architecture, reliability objectives, governance, and compliance.
- Contribute to AI platform strategy and collaborate with cross-functional teams, including mentoring colleagues.
What they ask for
- Bachelor’s degree in a listed or related field plus at least 4 years developing AI/ML algorithms or technologies, or a master’s degree in a listed or 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 and making cost, latency, throughput, and accuracy tradeoffs.
- Experience building agentic AI systems and workflows.
Pay
$197,300 - $225,100 USD base salary (McLean, VA; Cambridge, MA); $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 inference, agentic and multi-agent workflows, similarity search, guardrails, evaluation, governance, and optimization of scalable production AI systems.
Model-written summary · the original posting is authoritative