[Remote] Staff ML Infrastructure Engineer - Embodied AI

Note: The job is a remote job and is open to candidates in USA. General Motors is redefining mobility through innovative vehicle and experience design. They are seeking a Staff ML Infrastructure Engineer to develop and deploy machine learning solutions for autonomous driving, focusing on building scalable platforms that enhance model training and evaluation.

Responsibilities

• Lead the design, implementation, and deployment of scalable platforms and tools that drive machine learning model training and evaluation workflows across GM
• Own complex technical projects end-to-end, making key architectural decisions and technical trade-offs. You will be a core contributor to team planning, design reviews, and code quality
• Take a holistic view of projects, considering their impact across multiple teams, and across a longer timeline
• Proactively drive technical prioritization. Collaborate closely with partner teams to ensure maximum benefit from the systems we build
• Help shape our team through technical interviewing with high, well-calibrated standards, and play an essential role in recruiting
• Mentor and onboard junior engineers and interns, helping them grow their careers

Skills

• 5+ years of experience building large-scale distributed systems, applications, or advanced ML systems
• Proven track record of designing robust frameworks with high-quality, durable APIs
• Deep understanding of machine learning algorithms with hands-on application
• Expertise in building reliable, high-performance, and cost-efficient systems on modern cloud infrastructure
• End-to-end experience across the ML development lifecycle, including MLOps practices
• Strong cross functional collaboration skills across teams and organizations
• Exceptional coding skills in Python or C++
• Strong interest in autonomous driving and its transformative potential
• BS, MS, or PhD in Computer Science, Mathematics, or equivalent practical experience
• Experience with distributed training methodologies
• Experience scaling ML training across large GPU/CPU clusters or other accelerators
• Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow)
• Experience with performance profiling and state-of-the-art training optimization techniques, including their impact on model performance
• Experience with advanced build systems (e.g., Bazel, Buck, Blaze, CMake)
• Proficiency with 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

Company Overview

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