JPMorgan Chase
Sr Director of Software Engineering- AI Infrastructure Platforms
$233K–$325K
Seattle, WA +2 · Director · Full-time · Posted Oct 21, 2026
- Location
- Seattle, WA
Palo Alto, CA
San Francisco, CA - Experience
- 15+ years of engineering experience, including 8+ years of senior engineering leadership experience with responsibility for managing managers.
About the job
Leads engineering teams building and operating enterprise AI infrastructure platforms for training and experimentation across on-premises, public cloud, and accelerated-compute environments.
What you'll do
- Lead the design, delivery, and evolution of a Kubernetes-first AI infrastructure platform, including distributed training and inference support.
- Standardize developer workflows and build platform APIs, automation, scheduling, security, and operational capabilities.
- Lead multiple engineering teams and managers, align senior stakeholders, and drive reliability, GPU utilization, and capacity improvements.
What they ask for
- 15+ years of engineering experience, including 8+ years of senior engineering leadership managing managers.
- Experience delivering adopted, reliable platform products and leading large cross-functional teams in complex enterprise environments.
- Deep hands-on expertise with Kubernetes-based platforms, including multitenancy, RBAC, multi-cluster operations, controllers, operators, and platform APIs.
- Experience with AI training and experimentation platforms, PyTorch, distributed training, Ray or similar, GPU infrastructure, and hybrid cloud environments.
Nice to have
- Experience operating large-scale GPU fleets, including heterogeneous accelerators.
- Experience delivering hybrid AI platforms or working at the code level in large-scale distributed systems.
Pay
USD 232,750.00-$325,000.00 base salary (Seattle, WA); USD 232,750.00-$325,000.00 base salary (Palo Alto, CA); USD 232,750.00-$325,000.00 base salary (San Francisco, CA)
Tools
Kubernetes, PyTorch, Ray, Slurm, vLLM, NVIDIA
AI focus
Leads AI infrastructure platform engineering for training, experimentation, and related inference workloads; work includes distributed training, GPU scheduling and utilization, and platform support for serving constraints.
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