Senior Lead Software Engineer- AI​/ML Training Infrastructure

Seattle, WA, United States and 1 more

• Job Identification
• Job Category Software Engineering
• Business Unit Corporate Sector
• Locations 1301 2nd Ave, Seattle, WA, 98101, US 3223 Hanover St, Palo Alto, CA, 94304, US
• Job Schedule Full time

Be an integral part of an agile engineering team that is constantly pushing the boundaries of what is possible across cloud, infrastructure, and AI/ML platforms.

As aSenior Lead Software Engineer at JPMorgan

Chase within the Corporate Sector, Infrastructure Platforms team, you will play a critical role in designing, building, and operating secure, scalable, and resilient infrastructure platforms that power enterprise AI/ML workloads. You will help deliver market-leading technology products in a secure, stable, and highly available manner while driving meaningful business impact through deep technical expertise, engineering leadership, and strong problem-solving capabilities.

In this role, you will partner closely with AI/ML engineering teams, platform teams, product owners, and infrastructure stakeholders to translate complex compute, storage, networking, GPU, and scalability requirements into production-ready platforms formulti-GPU and multi-node model training. You will also help advance automation, developer productivity, responsible AI-assisted engineering practices, and operational excellence across the software delivery lifecycle.

Job 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 to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.

Required qualifications, capabilities and skills

• 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…