> Markdown version of [/jobs/ext/658005-senior-ml-inference-engineer-platform](https://www.wearedevelopers.com/jobs/ext/658005-senior-ml-inference-engineer-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Inference Engineer - Platform - **Company:** General Motors - **Location:** Olympia, WA, United States (Remote available) - **Experience:** Expert - **Salary:** $128,700.0 - $261,300.0 - **Contract:** Permanent contract - **Skills:** Airflow, Nvidia CUDA, Cursor (Graphical User Interface Elements), Programming Tools, Python (Programming Language), Machine Learning, Workflow Management Systems, Real Time Systems, GitHub Copilot, Pytorch, Large Language Models, Kubernetes, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Free and Open-Source Software, Machine Learning Operations, TensorRT - **Published:** June 26, 2026 - **Apply:** https://www.juju.com/job/00000000gb81ei ## About the Role + 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. ## 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., 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}. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies in Europe](https://www.wearedevelopers.com/magazine/162-highest-paying-tech-companies-in-europe)