Senior ML Platform Engineer — Large-Scale Training Infra

General Motors; Sunnyvale (CA); Hybrid; USD 170,000 - 241,000; Full time

Benefits offered by this job

GM vehicle discounts

Comprehensive benefits

Job summary

General Motors is seeking a Senior ML Engineer to design, build, and optimize scalable ML training infrastructure supporting AI research and sustainable autonomous driving initiatives. You will collaborate with ML engineers, researchers, and partners to deliver high-performance platforms across GM vehicles.

You will work in a hybrid setup, with travel to Sunnyvale, CA as needed, and contribute to cost-effective scaling of training across GPUs and cloud environments.

Qualifications

• Bachelors degree or higher in Computer Science or equivalent major OR equivalent relevant experience.
• 5+ years professional software engineering experience.
• 1+ years specialized experience in AI/ML infrastructure, e.g., enabling distributed training for scaling large ML models.

Responsibilities

• Design and development of scalable, reliable, high-performance ML framework to support model training at scale.
• Raise the bar on system observability, debuggability, and operational excellence, and user experience.
• Model training performance analysis and optimization solutions to scale distributed training workflows and maximize resource utilization across heterogeneous hardware environments, and save cost.
• Collaborate with cross-functional teams to integrate new features and technologies into the platform.

Skills

Python programming

Distributed training

Cloud environments

Collaboration

Strong debugging

Education

Bachelor's degree in Computer Science or equivalent

Tools

PyTorch

TensorFlow

Job description

General Motors is seeking a Senior ML Engineer to design, build, and optimize scalable ML training infrastructure supporting AI research and sustainable autonomous driving initiatives. You will collaborate with ML engineers, researchers, and partners to deliver high-performance platforms across GM vehicles.

You will work in a hybrid setup, with travel to Sunnyvale, CA as needed, and contribute to cost-effective scaling of training across GPUs and cloud environments.