General Motors is a global leader in advanced driverassistance, with Super Cruise hands-free technology in more than 500,000 equipped vehicles on the road and over 700 million hands-free miles driven—demonstratingthat automation can be trusted, intuitive, and helpful while reaching everyday drivers at unprecedented scale. Within GM AV, the Model Deployment & Inference Solutions team deploys machine learning models from training frameworks (e.g.,PyTorch) onto autonomous-vehicle hardware; our two-fold mission is to build the ML deployment platform that makes model rollouts fast and predictable, and to optimize models so they meet the real-time latency and memory budgets required to run on-vehicle. Our work sits on the critical path for GM27s publicly committed launch of eyes-off (hands-free, eyes-free) autonomous driving in 2028 on the Cadillac Escalade IQ, andwe27rehiring engineers to help deliver the next generation of safe, delightful personal autonomous-vehicle experiences.
As an early career Engineer on the Model Deployment & Inference Solutions team,you27llcontribute across both sides of our mission: building the ML deployment platform andoptimizingmodels for on-vehicle inference.You27llwork with and learn from senior engineers on real production deployments, platform features, and model-optimization workflows that ship to GM27s Super Cruise fleet at large scale, with structured mentorship and a clear onboarding plan.You27llalso collaborate closely with our sister teams (kernels,compiler, reduced precision, and parity) on the end-to-end path that takes trained models from research frameworks to ultra-efficient, safety-critical inference on the car.This is anearly-career/ new graduate role designed for candidates who have recently or will be completing their degree by June 2026.
WhatYou27llDo (Responsibilities)
Pair with senior engineers ondeployment workflows,performance investigations,model-optimization experiments(e.g., quantization, pruning, distillation), andplatform tooling.
Build, test, andmaintainplatform tools (e.g., validators, performance probes, parity and sensitivity analyzers, agentic specialists) with technical guidance and code review support.
Investigate and help root-cause production deployment or performance issues; learn and apply the diagnostic playbook forcompiler,kernel, runtime, and parity bugs.
Collaborate with cross-functional teams across the AV organization;including kernels, compiler, reduced-precision, parity, and model-development groups—to plan and execute model deployments to the AV stack, working under the guidance of senior engineers
