[Hiring] Director AI Engineering @Capital One

Role Description

At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent — along with our deep experience in machine learning — position us to be at the forefront of enterprises leveraging AI.

The Generative AI Training team builds the platform Capital One's scientists and engineers use to train, fine-tune, and experiment with foundation models at scale. We run the GPU clusters and distributed training infrastructure behind the company's generative AI.

• Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One.
• Oversee the design, development, testing, deployment, and operation of the platform's core systems.
• Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies.
• Invent and introduce state-of-the-art techniques to improve scalability, cost, throughput, and reliability.
• Own GPU capacity planning and cost governance.
• Contribute to the technical vision and long-term roadmap of foundational AI systems.
• Attract and retain top talent in the AI industry and nurture personal and professional development for your team.
• Translate the enterprise AI strategy into portfolio-level execution plans across multiple product areas.
• Scale AI engineering practices across teams through shared infrastructure, reusable components, and unified observability and governance frameworks.
• Establish enterprise standard for Responsible AI.
• Partner with research, compliance, and enterprise risk teams to ensure deployed systems meet emerging ethical and regulatory standards.

Qualifications

• Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in the same fields plus at least 6 years of experience.
• At least 3 years of people leadership experience.

Requirements

• 5+ years of experience managing and leading an engineering team.
• 7+ years of experience building and operating large-scale ML or GPU training infrastructure on cloud platforms.
• Hands-on experience with distributed training at scale.
• Experience with the ML orchestration and scheduling stack.
• Experience operating large GPU fleets with a focus on reliability and cost efficiency.
• Experience right-sizing GPU clusters and quotas to training and experimentation workload requirements.
• Excellent communication and presentation skills.
• Experience building and leading a multi-team AI organization.
• Proven ability to expand and execute long-term AI platform strategies.
• Experience establishing cross-functional operating rhythms and review cadences.

Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.