Summer Intern: ML Engineer for Edge AI Platform

General Motors; Sunnyvale (CA); On-site; USD 90,000 - 132,000; Full time

Job summary

General MotorsAV/AI Platform team is building a state-of-the-art AI training platform to accelerate autonomous driving development. This internship focuses on data processing, model training and inference workflows, and improving testing infrastructure to boost reliability and reduce latency.

You will work with engineers to develop scalable infrastructure, collect requirements for feature roadmaps, and implement code with C++ and Python while exploring distributed and accelerator-enabled ML

Qualifications

• Pursuing a Bachelor's or Master's degree in CS, AI, ML, robotics, or related STEM field.
• Graduating between Dec 2027 and Aug 2029 with at least one semester remaining.
• Available for full-time, 12 weeks in Summer 2027.
• Strong foundation in algorithms and data structures; proficient in C++ or Python.
• Experience in AI/ML coursework or projects.
• Strong communication and collaboration skills.

Responsibilities

• Develop scalable infrastructure and tools to support model training and deployment to edge devices, egression, and rules-based models, operations, and inference.
• Collect requirements with managers to create feature roadmap.
• Code deliverables with the engineering team.
• Adapt ML methods to exploit parallel environments (clusters, multicore, GPU).
• Perform responsibilities varying by team.

Skills

C++

Python

Algorithms

Data Structures

ML basics

Team Collaboration

Communication

Education

Bachelor's or Master's in CS/AI/Engineering

Tools

PyTorch

CUDA

Kubernetes

Job description

General MotorsAV/AI Platform team is building a state-of-the-art AI training platform to accelerate autonomous driving development. This internship focuses on data processing, model training and inference workflows, and improving testing infrastructure to boost reliability and reduce latency.

You will work with engineers to develop scalable infrastructure, collect requirements for feature roadmaps, and implement code with C++ and Python while exploring distributed and accelerator-enabled ML