[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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