Senior ML Inference Engineer - Platform

The 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 Role

This 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. 

• 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. 

• 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. 

• 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.

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