Staff ML Infrastructure Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Oversee the design, implementation, and deployment of scalable platforms and tools that facilitate machine learning model training and evaluation workflows throughout GM.

• Manage complex technical projects from start to finish, making critical architectural decisions and technical trade-offs.

• Serve as a key contributor in team planning, design evaluations, and ensuring code quality.

• Adopt a comprehensive perspective on projects, assessing their impact across various teams and over extended timelines.

• Actively promote technical prioritization.

• Work collaboratively with partner teams to maximize the benefits of the systems we develop.

• Contribute to shaping our team through technical interviews, maintaining high and well-calibrated standards, and play a pivotal role in recruitment efforts.

• Guide and onboard junior engineers and interns, assisting them in their career development.

⛳️ Requirements

• 5+ years of experience in developing large-scale distributed systems, applications, or advanced machine learning systems.

• Demonstrated success in designing robust frameworks with high-quality, resilient APIs.

• Profound understanding of machine learning algorithms with practical application experience.

• Expertise in creating reliable, high-performance, and cost-effective systems on contemporary cloud infrastructure.

• Comprehensive experience throughout the ML development lifecycle, including MLOps practices.

• Strong collaboration skills across teams and organizations.

• Exceptional coding proficiency in Python or C++.

• Keen interest in autonomous driving and its transformative capabilities.

• Bachelor’s, Master’s, or PhD in Computer Science, Mathematics, or equivalent practical experience.

• Preferred: Experience with distributed training methodologies.

• Experience in scaling ML training across extensive GPU/CPU clusters or other accelerators.

• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow).

• Experience in performance profiling and state-of-the-art training optimization techniques, as well as their impact on model performance.

• Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake).

• Proficient in containerization and orchestration technologies (e.g., Docker, Kubernetes).

🏝️ Benefits

• 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 and more.