Director AI Engineering (Remote Eligible)

Capital One; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Capital One is building responsible and reliable AI systems to transform banking. The Generative AI Training team runs GPU clusters and distributed training for foundation models at scale, enabling experiments with KServe and vLLM.

You will help shape the enterprise AI strategy and guide talent development. You will partner with engineers, scientists, and PMs to deploy, test, and operate core AI systems, balancing innovation with governance and risk considerations.

Qualifications

Responsibilities

• Partner with cross-functional team of engineers, researchers, and PMs to deliver AI-powered products.
• Oversee design, development, testing, deployment, and operation of the platform's core systems: distributed training and fine-tuning, RL workflows, GPU scheduling, resiliency, and self‑service environments.
• Make build-vs-buy decisions across Open Source and SaaS AI tech such as AWS Ultraclusters, HuggingFace, VectorDBs, PyTorch.
• Invent state-of-the-art techniques to improve scalability, cost, throughput, and reliability of large-scale distributed training.
• Own GPU capacity planning and cost governance across training workloads.
• Contribute to long-term technical vision and roadmap of foundational AI systems.
• Attract and retain top AI talent and foster a culture of learning and responsible AI.

Skills

Distributed systems

GPU training

MLOps

Open Source

Cloud platforms

Tools

PyTorch

HuggingFace

KServe

VectorDBs

AWS

Job 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. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build.

Team Description

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: fair-share scheduling across teams, large-scale fine-tuning and reinforcement learning, resilience for long-running jobs, and the utilization controls that keep that expensive hardware working efficiently. We also provide the self-service environments where teams deploy, serve, and evaluate models during experimentation, built on tools like KServe and vLLM. The platform we build is a central leverage point for AI across Capital One, shaping how fast and how affordable the rest of the company can build with foundation models.

What You’ll Do

• 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.

The Ideal Candidate

• You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good.
• Passion for staying abreast of the latest research,