Senior ML Infra Architect for AI Validation & Simulation

General Motors LLC; Sunnyvale (CA); Remote; USD 155,000 - 206,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

GM vehicle discounts

Job summary

General Motors LLC seeks a Senior ML Infrastructure engineer to design and scale robust compute platforms for Simulation workflows. You will focus on GPUs, scaling, and reliability while shaping the architecture and roadmap for GM's AI Validation / Simulation needs.

You will collaborate with Simulation and ML teams to translate workflows into platform requirements, drive performance monitoring, and explore new frameworks and hardware accelerators.

Qualifications

• 4+ years of industry experience in high‑performance backend services.
• Strong communication skills and ability to drive cross‑functional initiatives.
• Hands‑on experience with Cloud VM services (Google Compute Engine preferred).
• Experience with hardware‑in‑the‑loop validation systems and HPC preferred.

Responsibilities

• Design and implement core platform backend software components.
• Collaborate with Simulation and ML teams to map workflows to platform requirements.
• Lead decision‑making on Compute architecture, cloud capacity, caching, and auto‑scaling.
• Develop monitoring, observability, and metrics for reliability and performance.
• Research and integrate frameworks, accelerators, and distributed computing techniques.
• Lead large‑scale technical initiatives across GM’s ML infrastructure.
• Raise engineering standards through leadership and best practices.

Skills

Go

Distributed systems

Cloud platforms

Cross-functional work

GPU computing

Tools

Google Compute Engine

HPC tooling

Job description

General Motors LLC seeks a Senior ML Infrastructure engineer to design and scale robust compute platforms for Simulation workflows. You will focus on GPUs, scaling, and reliability while shaping the architecture and roadmap for GM's AI Validation / Simulation needs.

You will collaborate with Simulation and ML teams to translate workflows into platform requirements, drive performance monitoring, and explore new frameworks and hardware accelerators.