Senior ML Training Platform Engineer

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

Benefits offered by this job

Health benefits

Hybrid work arrangement

Relocation assistance

Job summary

General Motors Company is seeking a Senior ML Engineer to design and build scalable ML training infrastructure enabling advanced AI research for intelligent driving technologies. You will work with ML engineers, researchers, and cross-functional teams to deliver high-performance AI solutions.

The role emphasizes distributed training, observability, and cost-efficient resource utilization across cloud environments, with travel to Sunnyvale, CA as needed and a hybrid work model.

Qualifications

• Bachelor's 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 scalable, reliable ML training framework to support model training at scale.
• Improve observability, debuggability, and operational excellence.
• Analyze and optimize training performance across heterogeneous hardware and cloud environments.
• Collaborate with cross-functional teams to integrate new features into the platform.

Skills

Python programming

PyTorch

TensorFlow

Distributed computing

Cloud environments

Willingness to travel

Education

Bachelor's degree in Computer Science or related field

Tools

PyTorch 2.x+

Distributed training frameworks

Profiling & debugging tools

Job description

General Motors Company is seeking a Senior ML Engineer to design and build scalable ML training infrastructure enabling advanced AI research for intelligent driving technologies. You will work with ML engineers, researchers, and cross-functional teams to deliver high-performance AI solutions.

The role emphasizes distributed training, observability, and cost-efficient resource utilization across cloud environments, with travel to Sunnyvale, CA as needed and a hybrid work model.