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

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.

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.