Description
At Capital One, we are creating responsible and reliable AI systems, changing banking for good.
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.
Responsibilities:
- 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: distributed training and fine-tuning, reinforcement learning workflows, fair-share GPU scheduling, job resilience and fault tolerance, GPU utilization and efficiency, and self-service environments for model experimentation and evaluation.
- Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more.
- Invent and introduce state-of-the-art techniques to improve the scalability, cost, throughput, and reliability of large-scale distributed training and fine-tuning.
- Own GPU capacity planning and cost governance: right-size clusters, instance types, and quotas to the needs of training and experimentation workloads across teams.
- Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One.
- Attract and retain top talent in the AI industry and nurture personal and professional development for your team. Foster a culture of learning and staying abreast of the state-of-the-art in AI.
- Translate the enterprise AI strategy into portfolio-level execution plans across multiple product areas, balancing innovation with delivery discipline.
- Scale AI engineering practices across teams through shared infrastructure, reusable components, and unified observability and governance frameworks.
- Establish enterprise standard for Responsible AI, including fairness metrics, model evaluation protocols, documentation requirements, and audit readiness.
- Partner with research, compliance, and enterprise risk teams to ensure deployed systems meet emerging ethical and regulatory standards.
Requirements:
- 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 Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies.
- At least 3 years of people leadership experience.
Preferred Qualifications:
- 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: multi-node, multi-GPU jobs and parallelism strategies.
- Experience with the ML orchestration and scheduling stack.
- Experience operating large GPU fleets with a focus on reliability, fault tolerance, utilization, and cost efficiency.
Salary Information:
- Remote: $244,700 - $279,200 per year.
- Cambridge, MA: $269,100 - $307,200 per year.
- McLean, VA: $269,100 - $307,200 per year.
- New York, NY: $293,600 - $335,100 per year.
- San Francisco, CA: $293,600 - $335,100 per year.