Machine Learning Engineer, AI Inference Solutions (Early in Career)

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 GM’s publicly committed launch of eyes-off (hands-free, eyes-free) autonomous driving in 2028 on the Cadillac Escalade IQ, andwe’rehiring 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’llcontribute across both sides of our mission: building the ML deployment platform andoptimizingmodels for on-vehicle inference.You’llwork 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’llalso 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 August 2026. 

WhatYou’llDo (Responsibilities)

• Contribute production code across theML deployment platform,model-optimization workflows, andinferencebenchmarking/profiling infrastructure. 

• 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

• 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 practicesrequiredfor on-vehicle autonomous driving software.

Your Skills & Abilities (Required Qualifications)

• Recently completed orcompletingaBachelor’s orMaster’sdegree bySpring 2026 inComputer Science,ECE, or a relatedtechnical field. (Degree must becompleted before your start date.)

• Strongcomputer science fundamentals(e.g., data structures, algorithms, operating systems, computer architecture) and solid coding skills inPythonand/orC++,demonstratedthrough coursework, internships, or substantial projects.

• Hands-on experience inAI/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, orcompilers.

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

• Experience with—or strong interest in—usingcoding 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 workpartners

What Will Give You a Competitive Edge (Preferred Qualifications)

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

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

• Exposure tomodel optimization(quantization, pruning, distillation) orGPU profiling tools(Nsight Systems, Nsight Compute,PyTorchProfiler).

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

• Experience buildingagentic or LLM-powered tools or workflows.

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

• Coursework, projects, or publications touchingML systems(e.g.,MLSys, OSDI, ASPLOS, HPCA,NeurIPSsystems track).

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

Location

• Sunnyvale, CA

• This role is categorized ashybrid. 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 paidin accordance withapplicable state laws. The compensation may not be representative for positionslocatedoutside of New York, Colorado, California, or Washington. 

• The salary range for this roleis $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:  Anincentivepayprogram 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, tuitionassistanceprograms, employeeassistanceprogram, GM vehicle discounts and more.