2027 Intern - AI/ML Engineer - Capacity & Performance Engineering, Autonomous Vehicles
A 2027 AI/ML engineering internship on GM's Autonomous Vehicles Capacity & Performance Engineering team in Sunnyvale, CA (hybrid). The intern helps develop and optimize the team's capacity intelligence system—building scalable infrastructure, tools, and insights for model training, simulation, CI builds, and data generation—using Python, SQL, and BigQuery.
- Compensation
- $9.6K–$11.8K / mo
- Location
- Sunnyvale, Santa Clara County
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Currently enrolled full-time in a Bachelor's or Master's program in computer science or a related technical field
- Experience in systems software or algorithms
- Experience with modern object-oriented programming languages (e.g., Java, C++, Python); experience coding in Python
- Experience with SQL and BigQuery
- Strong communication skills with cross-functional team collaboration
- Able to work full-time, 40 hours per week
Nice to have
- Demonstrated software engineering experience via internship, work experience, or coding competitions
- Familiarity with AI-assisted engineering tools (code generation, model analysis, experiment planning)
- Demonstrated creativity and quick problem-solving capabilities
- Research and/or work experience in machine learning infrastructure
- Experience with distributed systems (e.g., Spark, Ray, Kubernetes, Slurm)
- Experience in scheduling algorithms, orchestration or compute
- Intent to return to degree program after internship
- Graduating between December 2027 and August 2028
Full description
Job DescriptionWork Arrangement:
Hybrid: This internship is categorized as a hybrid. The selected intern is expected to report to the office up to three times per week or as determined by the team.
Location:
Sunnyvale, California
About the Team:
The AV / AI Capacity & Performance Engineering team helps provide the right compute, networking, storage, and AI infrastructure to support AI and ML innovation supporting a wide range of use cases across teams such as Embodied AI, Simulation, Data Science, and more. The team builds capacity models, improves cloud efficiency, supports vendor planning, and develops tooling that makes capacity, demand, utilization, reliability, and cost visible to engineering stakeholders.
Our Capacity Intelligence system is being developed as the team’s operational interface and single point of truth for capacity intake, cloud spending, budget pacing, quota and queue analysis, egress forecasting, and related capacity insights. It is being expanded to incorporate new cloud providers and provide advanced modeling capabilities.
Our tools and systems empower GM to tackle the complexities of autonomous driving technology and expedite our path to commercialization.
About the Role:
As an intern, you will help develop and optimize our capacity intelligence system with a focus on integrating new capabilities.
What You’ll Do:
- Develop scalable infrastructure, tools and insights to support Model Training, Simulation, CI Build and Data Generation insights with the capacity intelligence program.
- Code deliverables in tandem with the engineering team.
- Perform specific responsibilities which vary by team.
Required Qualifications:
- 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 or a related technical field.
- Experience in systems software or algorithms.
- Experience with modern object-oriented programming languages (e.g., Java,C++, Python). Experience coding in Python.
- Experience with SQL, BigQuery
- Strong communication skills with experience collaborating across cross-functional teams.
- Able to work full-time, 40 hours per week.
Preferred Qualifications:
- 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).