Machine Learning Engineer, AI Inference Solutions (Early in Career)
About The Position
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 August 2026.
Requirements
- 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 reliability, 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
Nice To Haves
- 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.
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.
Benefits
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Associate degree
