Director AI Engineering (Remote Eligible)
$245k - $279k • Remote • New York, NY • Full-time
Posted 25d ago
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Infrastructure Engineer
About the job
Capital One is seeking a Director of AI Engineering to lead the development of responsible and reliable AI systems that are transforming the banking industry. This role involves overseeing the design, development, and operation of a platform that enables scientists and engineers to train, fine-tune, and experiment with foundation models at scale. You will be instrumental in shaping the future of AI at Capital One by driving innovation, ensuring scalability, and fostering a culture of learning and technical excellence. This position offers the opportunity to reimagine how we serve customers and businesses through the transformative power of emerging AI capabilities.
Responsibilities
- Partner with cross-functional teams to deliver AI-powered products.
- Oversee the design, development, testing, deployment, and operation of core platform systems for distributed training, fine-tuning, reinforcement learning, and GPU scheduling.
- Make build-vs-buy decisions for AI technologies like AWS Ultraclusters, Huggingface, VectorDBs, and PyTorch.
- Invent and introduce state-of-the-art techniques to improve scalability, cost, throughput, and reliability of large-scale distributed training and fine-tuning.
- Own GPU capacity planning and cost governance for training and experimentation workloads.
- Contribute to the technical vision and long-term roadmap of foundational AI systems.
- Attract, retain, and nurture top talent in the AI industry, fostering a culture of learning.
- Translate enterprise AI strategy into execution plans across multiple product areas.
- Scale AI engineering practices through shared infrastructure, reusable components, and unified observability and governance.
- Establish enterprise standards for Responsible AI, including fairness metrics, model evaluation, documentation, and audit readiness.
- Partner with research, compliance, and enterprise risk teams to ensure deployed systems meet 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.
- 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 (e.g., AWS, Google Cloud, Azure, or equivalent private cloud).
- Hands-on experience with distributed training at scale: multi-node, multi-GPU jobs and parallelism strategies.