Senior ML Inference Engineer - Platform

Job DescriptionAbout the TeamThe Model Deployment & Inference Solutions team in GM AV deploys machine learning models from training frameworks (e.g. PyTorch) onto autonomous vehicle hardware. Our mission is two-fold: build the ML deployment platform that makes model rollouts fast and predictable, and optimize models so they meet the real-time latency and memory budgets required to run on-vehicle. Our work is on the critical path of GM's publicly committed launch of eyes-off (hands-free, eyes-free) autonomous driving in 2028, debuting on the Cadillac Escalade IQ, building on Super Cruise's billion-plus hands-free miles.  About the RoleThis role sits in the team's Platform pillar. We own the unified ML deployment platform that automates the path from a trained model to inference on the vehicle, along with the developer-experience and agentic-tooling layer that makes deployment self-serve for every ML model development team at GM. What you’ll be doing (Responsibilities)Design, build, and operate the ML deployment platform that automates the path from trained model to on-vehicle inference. Drive cross-organization model deployments to the autonomous vehicle stack, partnering with model development teams to take high-value models from training to production on-vehicle. Build agentic tools that diagnose and fix deployment-blocking issues, automating workflows currently performed manually by engineers. Build the developer experience that ML model development teams use day to day: tooling, dashboards, automation, and observability. Drive shift-left validation that surfaces deployment risk (compile, runtime, parity, latency) early in the model development cycle. Build platform tools that integrate the work of our sister teams (kernels, compiler, reduced precision and parity) so their optimization wins land directly in the deployment workflow. Partner with the team's Performance pillar and model development teams across the AV organization. Your Skills & Abilities (Required Qualifications) BS, MS, or PhD in Computer Science or a related technical field. 3+ years of relevant industry experience. Strong fundamentals and excellent coding ability in Python. Experience building or operating production platform or infrastructure systems where reliability, observability, and extensibility matter. Experience with ML model deployment, inference integration, model optimization workflows, or model serving infrastructure, with at least one prior context where you owned the path from a trained model to a running inference workload. Experience using coding agents (Cursor, Claude Code, GitHub Copilot, or equivalent) as part of your engineering workflow. Experience designing clean, well-tested software with clear interfaces and good abstractions. Strong cross-team collaboration skills. What Will Give You A Competitive Edge (Preferred Qualifications)   Experience building agentic or LLM-powered developer tooling. Experience with ML or workflow orchestration frameworks (Airflow, Temporal, Flyte, Ray, Kubeflow, or equivalent). Familiarity with the NVIDIA GPU stack at the integration level (CUDA-aware Python, TensorRT, Triton inference server, torch.compile, ONNX). Experience with inference-serving frameworks (Triton, TorchServe, Ray Serve, vLLM) or edge-deployment toolchains. Experience with low-latency or real-time systems. Experience in autonomous vehicles, robotics, or other safety-critical ML deployment domains. Open-source contributions to PyTorch, Ray, Airflow, Temporal, vLLM, TensorRT, or related projects. 3+ years of relevant industry experience. Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.  The salary range for this role: is $128,700 to $261,300. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include 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.#GM-AV-1 This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}. The selected candidate will be required to travel About GMOur vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.Why Join Us We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.Benefits OverviewFrom day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.Non-Discrimination and Equal Employment Opportunities (U.S.)General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.All employment decisions are made on a non-discriminatory basis without regard to se