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General Motors Logo General Motors · IT - Software Development

Senior ML Inference Engineer - Platform

United States, Austin Employment contract 128700.00 - 261300.00 USD / Year · Job Posted May 14, 2026
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Job Description

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.

Job Responsibility

  • 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

Requirements

  • 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

Nice to have

  • 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

What we offer

  • 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
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