Senior Lead Engineer - AI/ML Training Infra & Cloud

JPMorgan Chase & Co.; Seattle (WA); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorganChase & Co. seeks a Senior Lead Software Engineer to design and operate secure, scalable infrastructure for enterprise AI/ML workloads across multi-GPU, multi-node training environments.

You will partner with AI/ML teams, platform engineers, and product owners to translate compute and networking needs into production-ready platforms. The role emphasizes automation, security, and operational excellence.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Hands-on experience building highly scalable and highly available infrastructure for machine learning training and/or inference workloads.
• Solid system-level understanding of GPU infrastructure, accelerators, high-speed interconnects, distributed compute, and related platform technologies.
• Strong experience with Kubernetes and containerization technologies, including Docker, cluster operations, workload scheduling, observability, and production troubleshooting.
• Proficiency in at least one modern programming language, such as Python, Go, Java, or C#.
• Demonstrated ability to independently solve complex design, scalability, reliability, performance, and functionality challenges with minimal oversight.
• Deep understanding of cloud component architecture, including microservices, compute, storage, networking, security, routing, and switching technologies.
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment...
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
• Design, develop, test, and deliver secure, high-quality production code; review, debug, and improve code written by others.

Responsibilities

• Design, develop, test, and deliver secure, high-quality production code; review, debug, and improve code written by others.
• Architect, build, and operate secure, scalable cloud infrastructure platforms optimized for multi-GPU and multi-node AI/ML training workloads.
• Partner with AI/ML, data science, and platform engineering teams to translate compute, storage, networking, GPU, and scalability needs into robust infrastructure requirements and platform capabilities.
• Drive technical design decisions that influence product architecture, application functionality, infrastructure strategy, and operational effectiveness.
• Monitor, manage, and optimize cloud and GPU infrastructure resources for performance, reliability, utilization, scalability, and cost efficiency.
• Build and maintain CI/CD pipelines, automation frameworks, and infrastructure-as-code solutions to streamline ML platform deployment, operations, and lifecycle management.
• Provide technical leadership and guidance to engineers, contractors, and vendor partners, ensuring solutions align with business priorities, engineering standards, security expectations, and long-term platform strategy.
• Apply deep knowledge of the Software Development Life Cycle toolchain, including enterprise-approved AI-assisted development and automation capabilities, to improve engineering productivity and automation at scale.
• Champion firmwide SDLC frameworks, engineering standards, secure coding practices, resiliency expectations, and operational best practices.
• Drives adoption and governance of approved AI-assisted engineering practices across teams...

Skills

Kubernetes

ML infrastructure

Python/Go/Java/C#

Problem solving

Education

Software engineering training or certification

Tools

Docker

Job description

JPMorganChase & Co. seeks a Senior Lead Software Engineer to design and operate secure, scalable infrastructure for enterprise AI/ML workloads across multi-GPU, multi-node training environments.

You will partner with AI/ML teams, platform engineers, and product owners to translate compute and networking needs into production-ready platforms. The role emphasizes automation, security, and operational excellence.