About the role#
As a Data Scaling Intern for Embodied AI, you will work on a project designed to improve the scale, quality, efficiency, or reliability of data and model development for autonomous driving. You will collaborate with researchers and engineers to build data pipelines, analyze large datasets, improve training workflows, and develop infrastructure that increases the number and quality of experiments the team can run.
What you'll do#
- Develop data pipelines and tooling for large-scale, multimodal autonomous driving datasets.
- Analyze data quality, coverage, distribution, and performance impact using quantitative methods.
- Prototype and evaluate approaches for data mining, curation, labeling, sampling, or scenario discovery.
- Improve training throughput, data loading, experiment reproducibility, and resource utilization in distributed computing environments.
- Collaborate with machine learning researchers, data engineers, infrastructure engineers, and autonomy teams.
- Build visualizations, metrics, dashboards, and evaluation workflows.
- Contribute to production-quality software through design reviews, code reviews, automated testing, continuous integration, and documentation.
- Present technical findings and document experiments, results, and recommendations.
What you'll need#
- Currently enrolled in or pursuing a Master’s or Ph.D. degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related technical field.
- Graduation date between December 2027 and June 2028, with an intent to return to a degree program after the internship.
- Demonstrated experience through coursework, research, academic projects, or professional work in machine learning, data engineering, or distributed systems.
- Strong programming skills in Python.
- Experience working with data processing, databases, machine learning pipelines, or large-scale datasets.
- Strong analytical and problem-solving skills.
- Ability to work collaboratively in a cross-functional environment.
- Strong written, verbal, and presentation skills.
- Availability to work full-time, 40 hours per week.
- Experience with machine learning frameworks like PyTorch, TensorFlow, or JAX.
- Experience with distributed training, parallel computing, high-performance computing, cloud infrastructure, or GPU-based workflows.
- Familiarity with multimodal sensor data, autonomous vehicles, robotics, computer vision, or large-scale time-series data.
- Experience with SQL, data warehouses, workflow orchestration, data quality systems, or distributed data-processing frameworks.
- Familiarity with foundation models, self-supervised learning, imitation learning, reinforcement learning, or deep learning.
- Experience with C++ or another systems programming language.
Location & details#
- Location: Sunnyvale, California.
- Term: Summer 2027.
- Work Modality: Hybrid (expected to be on-site 3 days a week).
- Employment: Full-time, paid internship.
- Note: Sponsorship is not provided for this role.
About General Motors
General Motors is a public company based in Detroit, Michigan. It operates in the motor vehicle manufacturing industry. The company employs over 106,000 people. Its work spans the design and production of cars, trucks, and transportation technology, including software, electrification, and AI.
How to get in at General Motors
Securing an internship at General Motors requires a proactive approach to the application timeline. Because early applicants are often reviewed before the volume of candidates becomes unmanageable, Intern Insider sends an instant alert the moment a role matching your target is published. This allows you to submit your materials among the first group of applicants. Reaching out to the right person can also change your outcome. Intern Insider surfaces the recruiters behind the company's roles, which lets you contact them directly to ask about the position or a potential referral. This direct connection often improves response rates compared to submitting an application into a general queue.
