Senior Director of Software Engineering, AI Infrastructure Platforms

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Seattle, WAPalo Alto, CASan Francisco, CA
Employment: Full-time
Posted: 6 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $217k

$153k $276k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $174,600–$259,125.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Senior Director of Software Engineering, AI Infrastructure Platforms

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The Sr Director of Software Engineering- AI Infrastructure Platforms leads multiple technical areas within the firmwide AI Infrastructure Platform organization. This role involves managing departments responsible for delivering a unified AI infrastructure layer across on-premises environments, public cloud, and accelerated-compute vendors. You will own the design and evolution of a Kubernetes-first AI infrastructure platform, focusing on training and experimentation. Key responsibilities include building platform APIs, Kubernetes controllers, and operators to standardize developer workflows and improve GPU availability. You will utilize technologies including Kubernetes, PyTorch, Ray, and vLLM while managing multi-tenancy, RBAC, and network policies. The role solves complex problems regarding distributed system design, GPU telemetry, and large-scale scheduling for AI workloads, ensuring reliable, secure, and operable systems across hybrid deployment environments.

Skills

Kubernetes PyTorch Ray vLLM GPU Infrastructure NVIDIA Distributed Training Slurm IAM RBAC API Design Multi‑tenancy Network Segmentation Observability SLIs SLOs

What you'll do

Lead the design and delivery of a Kubernetes-first AI infrastructure platform for enterprise-scale training and inference.
Standardize AI developer workflows to enable self-service job submission, reusable templates, and consistent runtime behavior.
Build platform APIs and automation, including Kubernetes controllers and operators, to ensure scalable and safe adoption.
Improve GPU availability and utilization through reliability engineering, fleet readiness patterns, and accelerated capacity onboarding.
Implement governance-based scheduling strategies including multitenant quotas, priority admission control, and topology-aware placement.
Embed enterprise-grade security, IAM controls, secrets management, and audit logging into all platform defaults.
Establish operational excellence by defining SLIs/SLOs, managing error budgets, and delivering end-to-end platform observability.
Manage multiple engineering teams and managers to build a high-performing organization with strong engineering standards.

What we're looking for

15+ years of engineering experience.
8+ years of senior engineering leadership experience with responsibility for managing managers.
Demonstrated experience delivering platform products with strong adoption, reliability, and operational maturity.
Experience developing and leading large, cross-functional engineering teams within highly matrixed and complex enterprise environments.
Deep hands-on expertise with Kubernetes-based platforms, including multi-tenancy, RBAC, controllers, operators, and platform API design.
Experience supporting AI training and experimentation platforms, including PyTorch, distributed training concepts, and Ray or similar frameworks.
Familiarity with Slurm or equivalent HPC or batch schedulers and core concepts such as queues, fair-share, and preemption.
Understanding of modern AI inference stacks, GPU infrastructure fundamentals, and cloud-native technologies in hybrid environments.
Experience operating large-scale GPU fleets, including heterogeneous accelerator environments (preferred).
Experience delivering hybrid AI platforms across on-premises infrastructure, public cloud, and specialized accelerated-compute vendors (preferred).
Experience working at the code level within large-scale distributed systems (preferred).

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