About the role#
The Data Scaling team builds the machine learning and infrastructure foundations that allow Embodied AI models to improve at scale. You will work on a scoped project 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, and improve infrastructure that increases the team's experimentation capacity.
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, and 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 to communicate data and model behavior.
- 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 degree in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related technical field.
- Must be graduating between December 2027 and June 2028 with the 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 and experience with C++ or another systems programming language.
- Experience working with data processing, databases, machine learning pipelines, or large-scale datasets.
- Strong analytical and problem-solving skills, with the ability to use quantitative analysis to guide decisions.
- Availability to work full-time, 40 hours per week, during the internship.
- Experience with machine learning frameworks like PyTorch, TensorFlow, or JAX.
- Familiarity with distributed training, high-performance computing, cloud infrastructure, or GPU-based workflows.
- Experience with SQL, data warehouses, workflow orchestration, or distributed data-processing frameworks.
- Familiarity with multimodal sensor data, autonomous vehicles, robotics, computer vision, or large-scale time-series data.
- Familiarity with foundation models, self-supervised learning, imitation learning, reinforcement learning, or deep learning.
Location & details#
- Location: Sunnyvale, California.
- Work Modality: Hybrid (on-site 3 days per week).
- Term: Summer 2027.
- Employment: Full-time paid internship.
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
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