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General Motors Logo General Motors · IT - Software Development

Staff Machine Learning Engineer - ML Training Infrastructure

United States, Austin Employment contract 185000.00 - 335300.00 USD / Year · Job Posted June 14, 2026
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Job Description

The Role:   We are seeking an experienced, technically strong, impact-driven expert in ML Training Infrastructure with a demonstrated ability to lead through hands-on technical work. In this role, you will be responsible for defining the technical direction and driving the design and development of scalable, reliable, and high-performance AI/ML platform infrastructure that enables advanced AI research and model development at scale. As a Staff ML Engineer, you will operate as a technical leader across initiatives, partnering closely with machine learning engineers, research scientists, and platform teams to shape architecture, drive major technical decisions, and deliver state-of-the-art AI infrastructure that enables the future of intelligent driving technologies across General Motors vehicles.

Job Responsibility

  • Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale
  • Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments
  • Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack
  • Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery
  • Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams
  • Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem
  • Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team

Requirements

  • Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience
  • 8+ years of professional software engineering experience
  • 5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models
  • Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems
  • Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure)
  • Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact
  • Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost
  • Willingness to travel to Sunnyvale, CA as needed
  • Comfortable operating in highly ambiguous and dynamic environments

Nice to have

  • 8+ years of professional software engineering experience
  • Deep expertise in PyTorch 2.x+ and distributed training frameworks
  • Experience designing and developing training platforms that support FSDP, pipeline parallelism, and other scalable solutions for training large foundational models
  • Experience profiling, analyzing, debugging, and optimizing training and data loading performance at scale
  • Strong record of technical leadership through architecture reviews, roadmap influence, and cross-team execution
  • Excellent communication skills, with the ability to build consensus, navigate controversial decisions, communicate risks clearly, and provide constructive technical feedback
  • Self-motivated, execution-oriented, and motivated by delivering broad organizational impact

What we offer

  • medical
  • dental
  • vision
  • Health Savings Account
  • Flexible Spending Accounts
  • retirement savings plan
  • sickness and accident benefits
  • life insurance
  • paid vacation & holidays
  • tuition assistance programs
  • employee assistance program
  • GM vehicle discounts
  • company vehicle evaluation program
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