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.