Senior Lead Software Engineer, AI Platforms

JPMorgan Chase

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Work type: On-site
Location: Seattle, WA
Posted: 4 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $199k

$140k most similar roles pay here $262k

This listing doesn't post a salary. Most similar roles pay $151,991–$246,150.

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 1138 open roles on FindRole.

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At a glance

TL;DR · Senior Lead Software Engineer, AI Platforms

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As a Senior Lead Software Engineer, AI Platforms within the Infrastructure Platforms team, you will design, build, and operate secure, scalable infrastructure platforms specifically optimized for multi-GPU and multi-node machine learning training workloads. You will collaborate with AI/ML engineering teams to translate complex compute, storage, networking, and GPU requirements into production-ready capabilities while driving automation and developer productivity through the software delivery lifecycle. Your daily work involves developing high-quality code, managing CI/CD pipelines, and implementing infrastructure-as-code solutions. The role requires expertise in Kubernetes, Docker, and programming languages like Python, Go, Java, or C#. You will also manage GPU infrastructure resources and oversee AI-assisted engineering practices. Technical focus areas include distributed systems, high-performance computing, MLOps tools like MLflow, and specialized NVIDIA GPU software ecosystems to solve complex scalability and reliability challenges in enterprise environments.

Skills

AI/ML Kubernetes Docker Python Go Java C# CI/CD infrastructure-as-code CUDA NVIDIA GPU MLflow Ray.io Slurm Distributed Systems Microservices Linux High-Performance Computing SDLC

What you'll do

Architect and operate secure, scalable cloud infrastructure platforms optimized for multi-GPU and multi-node AI/ML training workloads.
Translate complex compute, storage, networking, and GPU requirements into production-ready infrastructure capabilities.
Develop, test, and deliver high-quality production code while performing peer code reviews and debugging.
Build and maintain CI/CD pipelines and infrastructure-as-code solutions to automate ML platform deployment and operations.
Monitor and optimize cloud resources for performance, reliability, utilization, scalability, and cost efficiency.
Lead technical design decisions regarding product architecture, infrastructure strategy, and operational effectiveness.
Drive the adoption of approved AI-assisted engineering practices to improve code quality and delivery speed.
Provide technical leadership and guidance to engineers and partners to ensure alignment with security and engineering standards.

What we're looking for

Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience building scalable infrastructure for machine learning training and inference workloads.
System-level understanding of GPU infrastructure, accelerators, high-speed interconnects, and distributed compute technologies.
Strong experience with Kubernetes, Docker, containerization, and production troubleshooting.
Proficiency in at least one modern programming language such as Python, Go, Java, or C#.
Deep understanding of cloud architecture including microservices, storage, networking, security, routing, and switching.
Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools for engineering productivity.
Experience with NVIDIA GPU infrastructure, MLOps platforms, high-performance computing, and distributed systems (preferred).