Summer Intern – Machine Learning Engineer, AV/AI Platform

General Motors

. Develop scalable infrastructure and tools supporting model training, model deployment to edge devices, egression, rules-based models, operations, and inference .

Posted 10/2/2026internshipSunnyvale • California • United StatesEntry Level💰 $8,100 - $11,800 per monthWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong proficiency in C++ and Python for developing scalable AI infrastructure and tools, with a solid foundation in algorithms and data structures. Experience in machine learning, deep learning, and familiarity with distributed systems and ML infrastructure is essential.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingMachine Learning ExperienceAlgorithms and Data StructuresDistributed Systems Exposure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

C++ ProgrammingPython ProgrammingAlgorithmsData StructuresMachine LearningDeep LearningReinforcement LearningNLPComputer VisionData Mining

Soft Skills

Strong Communication SkillsCollaboration

Tools & Technologies

SparkRayKubernetesSlurmPyTorchTensorRTONNXKubeflowCUDAOpenCL

Industry Keywords

Artificial IntelligenceMachine LearningSelf-Driving TechnologyPattern RecognitionSignal Processing

Tech Stack

Tools & technologies

Distributed SystemsKubernetesPythonPyTorchRaySparkC++

About the role

Key responsibilities & impact

Develop scalable infrastructure and tools supporting model training, model deployment to edge devices, egression, rules-based models, operations, and inference
Develop and optimize the AI training platform
Improve data processing pipelines
Accelerate model training and inference workflows
Enhance testing infrastructure
Support tool development, instrumentation, and system monitoring to boost reliability, reduce latency, and increase iteration speed
Suggest, collect, and synthesize requirements with managers or tech leads to create feature roadmaps
Code deliverables in tandem with the engineering team
Adapt standard machine learning methods to exploit distributed clusters, multicore SMP, and GPU environments
Perform team-specific responsibilities

Requirements

What you’ll need

Currently enrolled in a full-time, degree-seeking program and 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 after the internship
Available to work full-time, 40 hours per week, during the 12-week summer internship period between May 24–August 13 or June 14–September 3, 2027
Strong foundation in algorithms and data structures
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 ability to collaborate across teams
Preferred: software engineering experience through an internship, work experience, coding competition, student engineering team, research, coursework, or personal project
Preferred: familiarity with AI-assisted engineering tools
Preferred: research and/or work experience in machine learning, deep learning, reinforcement learning, NLP, recommendation systems, pattern recognition, signal processing, data mining, artificial intelligence, or computer vision
Preferred: exposure to distributed systems and ML infrastructure such as Spark, Ray, Kubernetes, Slurm, PyTorch, TensorRT, ONNX, Kubeflow, or similar technologies
Preferred: experience with CUDA, OpenCL, Triton, or other accelerator programming languages
Preferred: passion for self-driving technology and its potential impact

Benefits

Comp & perks

One-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program to help facilitate relocation benefits
Hybrid work arrangement
Benefits and well-being support from day one