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General Motors · 4 hours ago

2026 Summer Intern – Machine Learning Systems Engineer – Autonomous Driving (Bachelor's Degree)

Sunnyvale, CA

Internship

Hybrid

Intern

$7,300/mo - $8,600/mo

Start in 2026 Summer

General Motors is a leading automotive company focused on innovation and safety. The role involves developing and optimizing AI training platforms to enhance autonomous driving performance through improved data processing pipelines and model training workflows.

AutomotiveElectric VehicleInformation ServicesManufacturingTransportation

No H1B

Responsibilities

Develop scalable infrastructure and tools to support model training, regression, and rules-based models

Suggest, collect and synthesize requirements and create effective feature roadmap

Code deliverables in tandem with the engineering team

Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)

Perform specific responsibilities which vary by team

Qualification

Machine LearningDeep LearningPythonDistributed SystemsObject-Oriented ProgrammingCommunication SkillsProblem-SolvingCollaboration

Required

Currently enrolled in a full-time, degree-seeking program and in the process of obtaining a Bachelor's degree in computer science or a related technical field

Research and/or work experience in a relevant field, such as machine learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision

Experience in systems software or algorithms

Experience with modern object-oriented programming languages (e.g., Java, C++, Python)

Strong communication skills with experience collaborating across cross-functional teams

Able to work fulltime, 40 hours per week

Preferred

Intent to return to degree-program after the completion of the internship

Demonstrated software engineer experience via an internship, work experience, coding competitions

Familiarity with AI –assisted engineering tools (e.g., for code generation, model analysis, or experiment planning) to enhance productivity and understanding of ML systems

Demonstrated creativity and quick problem-solving capabilities

Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm)

Experience in CUDA, OpenCL, Triton or other accelerator programming language

Graduating between December 2026 and August 2027