Director, AI Engineering

Capital One

. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .

Posted 10/6/2026full-timeRemote • United StatesLead💰 $244,700 - $279,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI and ML algorithm development, large-scale distributed training infrastructure, and GPU capacity planning. Proven ability to lead engineering teams, establish Responsible AI standards, and execute enterprise-level AI strategies.

Highest-signal resume keywords

AI And ML Algorithm DevelopmentLarge-Scale Distributed Training InfrastructureGPU Capacity PlanningPeople Leadership ExperiencePython Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI TechnologiesML AlgorithmsDistributed TrainingGPU SchedulingPythonGoC++CUDAML OrchestrationParallelism Strategies

Soft Skills

Excellent CommunicationPresentation SkillsMentoring

Tools & Technologies

AWS UltraclustersHugging FaceKubernetesKubeflowSlurmRayKServeVLLMDeepSpeedMegatron

Industry Keywords

Responsible AI StandardsEthical StandardsRegulatory FrameworksAI StrategyCost Efficiency

Tech Stack

Tools & technologies

AWSCloudKubernetesNode.jsPythonPyTorchRayC++Go

About the role

Key responsibilities & impact

Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
Oversee design, development, testing, deployment, and operation of distributed training, fine-tuning, reinforcement learning, GPU scheduling, fault tolerance, utilization, and model experimentation systems
Make build-versus-buy decisions across open-source and SaaS AI technologies including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch
Introduce techniques improving scalability, cost, throughput, and reliability of large-scale distributed training and fine-tuning
Own GPU capacity planning and cost governance across teams
Contribute to the technical vision and long-term roadmap of foundational AI systems
Attract, retain, mentor, and develop AI engineering talent
Translate enterprise AI strategy into portfolio-level execution plans across product areas
Scale AI engineering practices through shared infrastructure, reusable components, observability, and governance frameworks
Establish enterprise Responsible AI standards covering fairness metrics, model evaluation, documentation, and audit readiness
Partner with research, compliance, and enterprise risk teams on ethical and regulatory standards

Requirements

What you’ll need

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 Master's degree in these fields plus at least 6 years of such experience
At least 3 years of people leadership experience
5+ years of experience managing and leading an engineering team (preferred)
7+ years of experience building and operating large-scale ML or GPU training infrastructure on cloud platforms (preferred)
Hands-on experience with distributed training at scale, including multi-node, multi-GPU jobs and parallelism strategies such as PyTorch FSDP, DeepSpeed, and Megatron
Proficiency in Python, Go, C++, or CUDA
Experience with ML orchestration and scheduling tools such as Kubernetes, Kubeflow, Kueue, Slurm, Ray, KServe, and vLLM
Experience operating large GPU fleets with focus on reliability, fault tolerance, utilization, and cost efficiency
Experience right-sizing GPU clusters, instance types, interconnect, and quotas
Passion for current AI and ML-systems research and applying novel training and optimization techniques
Excellent communication and presentation skills
Experience building and leading a multi-team AI organization
Ability to execute long-term AI platform strategies aligned with enterprise priorities and regulatory frameworks
Experience establishing cross-functional operating rhythms and review cadences
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
Employment authorization sponsorship may be considered for a new qualified applicant
Reasonable accommodations for applicants with disabilities