2027 Summer Intern – Machine Learning Engineer, AV/AI Platform

General Motors • Sunnyvale, CA

1d • $8,100 - $11,800 • Hybrid

About The Position

GM’s AV/AI Platform organization builds the software foundation and engineering tools that power safe, reliable, and scalable autonomous driving and advanced driver-assistance systems. We are building a state-of-the-art AI training platform in close collaboration with model teams to deliver Autonomous car milestones, maximizing model flop utilization and iteration speed, and enhancing inference systems. We are augmenting training, data processing, evaluation and the deployment loop with a data flywheel to validate performance and continuously improve Autonomous driving and Active Safety. We accelerate compute performance through architectural changes and low-level optimizations, informed by advanced HIL platforms, AI tooling and workflows and instrumentation built in-house. We streamline compiler/build and modelbench performance; enhance data processing, preprocessing, and batch inference; and improve monitoring and availability to increase velocity and quality.
As an intern, you will help develop and optimize our AI training platform by improving data processing pipelines, accelerating model training and inference workflows, and/ or enhancing testing infrastructure. You will support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed for advancing autonomous driving performance.

Requirements

• Currently enrolled in a full-time, degree-seeking program and in the process of obtaining a Bachelor's or Master's degree in computer science, artificial intelligence, machine learning, robotics, engineering, or a related STEM field.
• Must be graduating between December 2027 and August 2029 and have at least one additional quarter/semester of school remaining following the completion of the internship.
• Availability to work full-time, 40 hours per week, during the 12 week summer internship period between May 24th – August 13th or June 14th – September 3rd 2027
• Strong foundation in algorithms and data structures, with proficiency in C++, Python, or another object-oriented programming language.
• Demonstrated coursework, project, research, or applied experience in artificial intelligence or machine learning
• Experience with algorithms, software development, or systems engineering fundamentals
• Strong written and verbal communication skills and the ability to collaborate across teams.

Nice To Haves

• Demonstrated software engineering experience through an internship, work experience, coding competition, student engineering team or competition (e.g., SAE student competitions, EcoCAR, Formula SAE, or Baja SAE), research, coursework, or personal project. Relevant experience may include vehicle systems, autonomy, robotics, embedded software, controls, simulation, sensor data, or ML; exposure to AI-assisted development tools, agentic workflows, or LLM-powered tools is a plus.
• 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.
• 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 programming in C++, Python, or another object-oriented language
• Exposure to distributed systems and ML infrastructure, such as Spark, Ray, Kubernetes, Slurm, PyTorch, TensorRT, ONNX, Kubeflow, or similar technologies, and/or accelerator programming with CUDA, OpenCL, Triton, or related tools.
• Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm)
• Experience in CUDA, OpenCL, Triton or other accelerator programming language.
• Passion for self-driving technology and its potential impact on the world

Responsibilities

• Develop scalable infrastructure and tools to support model training, model deployment to the edge devices, egression, and rules-based models, operations, and inference.
• Suggest, collect and synthesize requirements and work with your managers or tech leads to create 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.

Benefits

• GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program - to help facilitate administration of relocation benefits, please apply using the permanent address you would move from.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern