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 Role   

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 related technical 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. 

• Depth in at least one of: computer architecture, operating systems, distributed systems, or compilers. 

• Demonstrated software-engineering experience (internships, coursework, open-source, research code, or competitions) showing good judgment around  r eliability, correctness, and clean abstractions. 

• Experience with—or strong interest in—using coding assistants/agents (e.g., Cursor, Claude Code, GitHub Copilot) as part of your workflow. 

• Ability to work effectively in collaborative, cross-functional teams and communicate clearly—both in writing and verbally—including explaining technical work partners 

What Will Give You a Competitive Edge (Preferred Qualifications)  

• Internship, research, or advanced coursework in ML systems, ML compilers, GPU programming (CUDA, OpenAI Triton), inference optimization, or distributed training/serving infrastructure. 

• Familiarity with PyTorch and modern ML compiler/runtime stacks (e.g., torch.compile, TensorRT, ONNX, Triton Inference Server, vLLM, or equivalent). 

• Exposure to model optimization (quantization, pruning, distillation) or GPU profiling tools (Nsight Systems, Nsight Compute, PyTorch Profiler). 

• Familiarity with workflow/ML platforms such as Airflow, Temporal, Flyte, Ray, or Kubeflow. 

• Experience building agentic or LLM-powered tools or workflows. 

• Open-source contributions related to PyTorch, TensorRT, vLLM, OpenAI Triton, or similar projects. 

• Coursework, projects, or publications touching ML systems (e.g., MLSys, OSDI, ASPLOS, HPCA, NeurIPS systems track). 

• Familiarity with a systems language (e.g., C++) and development in a Linux environment. 

Location  

• Sunnyvale, CA 

• This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week.  

• This job may be eligible for relocation benefits 

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.      

• The salary range for this role   is $119,250 to $150,850. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.  

• 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.