C

Capital One

Issues credit cards and runs a consumer bank

Director AI Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a related field plus at least 8 years of experience developing AI and machine learning algorithms or technologies, or a master's degree in one of those fields plus at least 6 years of such experience.
  • At least 3 years of people leadership experience.
  • Experience managing and leading an engineering team for at least 5 years is preferred.
  • At least 7 years of experience building and operating large-scale machine learning or GPU training infrastructure on cloud platforms or equivalent private cloud is preferred.
  • Hands-on experience with distributed training at scale, including multi-node, multi-GPU jobs and parallelism strategies such as PyTorch FSDP, DeepSpeed, or Megatron, and proficiency in Python, Go, C++, or CUDA is preferred.
  • Experience with machine learning orchestration and scheduling tools such as Kubernetes, Kubeflow, Kueue, Slurm, Ray, KServe, or vLLM is preferred.
  • Experience operating large GPU fleets with a focus on reliability, fault tolerance, utilization, and cost efficiency is preferred.
  • Experience right-sizing GPU clusters, instance types, interconnect, and quotas to training and experimentation workload requirements is preferred.
  • Excellent communication and presentation skills, including the ability to explain complex AI and infrastructure concepts to peers, are preferred.
  • Experience building and leading a multi-team AI organization delivering multiple enterprise capabilities concurrently is preferred.
  • Proven ability to expand and execute long-term AI platform strategies aligned to enterprise priorities and regulatory frameworks is preferred.
  • Experience establishing cross-functional operating rhythms and review cadences, such as OKRs, AI governance councils, and quarterly reviews, is preferred.

Responsibilities

  • Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products for associates and customers.
  • Oversee the design, development, testing, deployment, and operation of core platform systems, including 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 build-versus-buy decisions across open-source and SaaS AI technologies, including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch.
  • Develop techniques to improve the scalability, cost, throughput, and reliability of large-scale distributed training and fine-tuning.
  • Own GPU capacity planning and cost governance by sizing clusters, instance types, and quotas to training and experimentation workloads across teams.
  • Contribute to the technical vision and long-term roadmap of foundational AI systems.
  • Attract and retain AI talent and nurture the team's personal and professional development; foster a culture of learning and engagement with state-of-the-art AI.
  • Translate 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 standards 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.

Desired Qualifications

  • Stay current with AI and machine learning research, understand scientific publications, and judiciously apply novel techniques in production.
  • Apply distributed training and optimization techniques informed by current AI and machine learning systems research.

About the Company

Capital One

Issues credit cards and runs a consumer bank

Capital One runs a consumer and commercial bank that issues credit cards, takes deposits, and finances cars and businesses. Its Venture travel cards and Quicksilver cash-back cards let customers earn rewards on everyday spending, while its online banking tools manage payments and savings. Customers use checking and savings accounts through an app, branches, and cash machines, and commercial clients use treasury tools to manage company cash. The bank earns interest on loans and fees from banking and card transactions, with customer deposits helping fund its lending operations. Capital One was founded in 1994 and has its headquarters in McLean, Virginia.

Company size
10,001+
Company stage
IPO
Headquarters
McLean, VA
Founded
1994

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