Machine Learning Engineer, AI… at General Motors · Sunnyvale

Summary: General Motors is a global leader in advanced driver assistance technologies, and they are seeking a Machine Learning Engineer for their Model Deployment & Inference Solutions team. This early-career role involves contributing to the ML deployment platform and optimizing models for on-vehicle inference, working closely with senior engineers and cross-functional teams. Responsibilities: - Contribute production code across the ML deployment platform, model-optimization workflows, and inference benchmarking/profiling infrastructure - Pair with senior engineers on deployment workflows, performance investigations, model-optimization experiments (e.g., quantization, pruning, distillation), and platform tooling - Build, test, and maintain platform tools (e.g., validators, performance probes, parity and sensitivity analyzers, agentic specialists) with technical guidance and code review support - Investigate and help root-cause production deployment or performance issues; learn and apply the diagnostic playbook for compiler, kernel, runtime, and parity bugs - Collaborate with cross-functional teams across the AV organization; including kernels, compiler, reduced-precision, parity, and model-development groups—to plan and execute model deployments to the AV stack, working under the guidance of senior engineers - Participate in code reviews, design discussions, and technical documentation to ensure reliability, correctness, and clear abstractions in a large-scale codebase - Learn and follow secure coding, safety, and compliance practices required for on-vehicle autonomous driving software Required Qualifications: - Recently completed or completing a Bachelor's or Master's degree by Spring 2026 in Computer Science, ECE, or a related technical field. (Degree must be completed before your start date.) - Strong computer science fundamentals (e.g., data structures, algorithms, operating systems, computer architecture) and solid coding skills in Python and/or C++, demonstrated through coursework, internships, or substantial projects - Hands-on experience in AI/ML (e.g., machine learning, deep learning, computer vision, NLP, or ML systems) via classes, research, internships, or personal projects - Depth in at least one of: computer architecture, operating systems, distributed systems, or compilers - Demonstrated software-engineering experience (internships, coursework, open-source, research code, or competitions) showing good judgment around reliability, correctness, and clean abstractions - Experience with—or strong interest in—using coding assistants/agents (e.g., Cursor, Claude Code, GitHub Copilot) as part of your workflow - Ability to work effectively in collaborative, cross-functional teams and communicate clearly—both in writing and verbally—including explaining technical work partners Preferred Qualifications: - Internship, research, or advanced coursework in ML systems, ML compilers, GPU programming (CUDA, OpenAI Triton), inference optimization, or distributed training/serving infrastructure - Familiarity with PyTorch and modern ML compiler/runtime stacks (e.g., torch.compile, TensorRT, ONNX, Triton Inference Server, vLLM, or equivalent) - Exposure to model optimization (quantization, pruning, distillation) or GPU profiling tools (Nsight Systems, Nsight Compute, PyTorch Profiler) - Familiarity with workflow/ML platforms such as Airflow, Temporal, Flyte, Ray, or Kubeflow - Experience building agentic or LLM-powered tools or workflows - Open-source contributions related to PyTorch, TensorRT, vLLM, OpenAI Triton, or similar projects - Coursework, projects, or publications touching ML systems (e.g., MLSys, OSDI, ASPLOS, HPCA, NeurIPS systems track) - Familiarity with a systems language (e.g., C++) and development in a Linux environment Required Skills: Computer Science Fundamentals, Python, C++, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, ML Systems, Computer Architecture, Operating Systems, Distributed Systems, Compilers 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