Senior AI Researcher: Foundation Models & Applied ML

Capital One; San Jose (CA); On-site; USD 239,000 - 272,000; Full time

Job summary

Capital One seeks an Applied Researcher 4 to advance AI foundations, scale models, and deliver real-time AI-powered banking experiences. You will collaborate with data scientists, engineers and product teams to turn research into production-ready solutions.

You will work with PyTorch, AWS, HuggingFace, and cloud platforms, build foundation models, and publish or present findings. A PhD or MS with research background and a track record in ML theory or large-scale training is preferred.

Qualifications

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields or MS + 2 years of Applied Research.
• Experience in applied research and ML.

Responsibilities

• Partner with cross-functional teams to deliver AI-powered products that change how customers interact with their money.
• Leverage technologies like PyTorch, AWS Ultraclusters, HuggingFace, Lightning to extract insights from large datasets.
• Build AI foundation models through all development phases: design, training, evaluation, and deployment.
• Engage in high-impact applied research to translate AI advances into customer-facing solutions.
• Translate complex work into tangible business goals.
• Collaborate with senior researchers to prepare internal reports or conference submissions.

Education

PhD in Electrical Engineering or related field

MS in Electrical Engineering or related field + 2 years Applied Research

Job description

Capital One seeks an Applied Researcher 4 to advance AI foundations, scale models, and deliver real-time AI-powered banking experiences. You will collaborate with data scientists, engineers and product teams to turn research into production-ready solutions.

You will work with PyTorch, AWS, HuggingFace, and cloud platforms, build foundation models, and publish or present findings. A PhD or MS with research background and a track record in ML theory or large-scale training is preferred.