Machine Learning Engineer, AI Inference Solutions (University Grad)

General Motors is a global leader in advanced driver   assistance , with Super Cruise hands-free technology in more than 500,000 equipped vehicles on the road and over 700 million hands-free miles driven— demonstrating   that 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 GM’s publicly committed launch of eyes-off (hands-free, eyes-free) autonomous driving in 2028 on the Cadillac Escalade IQ, and   we’re   hiring engineers to help deliver the next generation of safe, delightful personal autonomous-vehicle experiences.   About the R o le    As an early career Engineer on the Model Deployment & Inference Solutions team,   you’ll   contribute across both sides of our mission: building the ML deployment platform and   optimizing   models for on-vehicle inference.   You’ll   work with and learn from senior engineers on real production deployments, platform features, and model-optimization workflows that ship to GM’s Super Cruise fleet at large scale, with structured mentorship and a clear onboarding plan.   You’ll   also 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 an   early-career   / new graduate role designed for candidates who have recently or will be completing their degree by June 2026.    What   You’ll   Do (Responsibilities)   Contribute production code across the     ML deployment platform ,   model-optimization workflows , and   inference   benchmarking/profiling infrastructure.    Pair with senior engineers on   deployment workflows ,   performance investigations ,   model-optimization experiments   (e.g., quantization, pruning, distillation), and   platform tooling .   Build, test, and   maintain   platform 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 for   compiler ,   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   Participate in code reviews, design discussions, and technical documentation to ensure reliability, correctness, and clear abstractions in a large-scale codebase.   Learn and follow secure coding, safety, and compliance practices   required   for on-vehicle autonomous driving software.     Your Skills & Abilities ( Required Qualifications )   Recently completed or   completing   a   Bachelor’s or   Master’s   degree by   Spring 2026 in   Computer Science ,   ECE , or a rela ted   t echnica l field . (Degree must be   completed before your start date. )   Strong   computer science fundamentals   (e.g., data structures, algorithms, operating systems, computer architecture) and solid coding skills in   Python   and/or   C++ ,   demonstrated   through coursework, internships, or substantial projects.   Hands-on experience in   AI/ML   (e.g., machine learning, deep learning, computer vision, NLP, or ML systems) via classes, research, internships, or personal projects.