Senior ML Inference Engineer - Platform

• Job Description
• About the Team
• 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.
• What
• you'll
• be doing (Responsibilities)
• + Design, build, andoperatethe 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, reducedprecisionand 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 toPyTorch, 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