Note: The job is a remote job and is open to candidates in USA. General Motors is a company focused on leading the change towards a world with Zero Crashes, Zero Emissions, and Zero Congestion. They are seeking a Senior ML Inference Engineer to design and build the ML deployment platform for autonomous vehicles, ensuring efficient model rollouts and optimizing performance for real-time applications.
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
Skills
- 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
Benefits
- 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.
- This job may be eligible for relocation benefits.
Company Overview