Senior ML Infrastructure Engineer, Compute — Remote

General Motors; Mountain View (CA); Híbrido; USD 155.000 - 206.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Relocation benefits

GM benefits package

Descripción de la vacante

General Motors seeks a Senior ML Infrastructure engineer to design and scale compute platforms for simulation workflows across GM's AI Validation Platform. You will optimize GPU utilization, build reliable distributed systems, and drive architectural decisions that enable rapid experimentation for autonomous driving research.

The role requires strong backend engineering skills, deep knowledge of Go and cloud platforms (GCP/AWS/Azure), and a bias for action in a fast-paced, cross-functional

Formación

• 4+ years of experience building high-performance backend services.
• Strong expertise in Go or similar languages.
• Experience with cloud platforms such as GCP, Azure, or AWS.
• Experience delivering cross-functional initiatives.
• Strong communication skills and ability to drive cross-functional initiatives.
• Thrive in a dynamic, multi-tasking environment with evolving priorities.

Responsabilidades

• Design and implement core platform backend software components.
• Collaborate with Simulation/ML engineers to translate workflows into platform requirements.
• Lead technical decision-making on Compute architecture, cloud capacity provisioning, caching, and auto-scaling.

Conocimientos

Go

Cloud platforms

Distributed systems

Backend services

Cross-functional collaboration

Communication skills

Herramientas

Google Compute Engine

Descripción del empleo

General Motors seeks a Senior ML Infrastructure engineer to design and scale compute platforms for simulation workflows across GM's AI Validation Platform. You will optimize GPU utilization, build reliable distributed systems, and drive architectural decisions that enable rapid experimentation for autonomous driving research.

The role requires strong backend engineering skills, deep knowledge of Go and cloud platforms (GCP/AWS/Azure), and a bias for action in a fast-paced, cross-functional