Embodied AI Data Scaling Intern

General Motors (GM); Sunnyvale (CA); On-site; USD 132,000 - 163,000; Full time

Benefits offered by this job

Paid holidays

Vehicle discount program

Mentorship and hands-on experience

Job summary

General Motors in Sunnyvale, California, is seeking an Embodied AI Data Scaling Intern to help build data pipelines and ML infrastructure for autonomous driving. The role is hybrid with on-site reporting three days a week.

You will work with researchers to improve data quality, training workflows, and experiment scalability across multimodal datasets, with collaboration across ML, data engineering, and infrastructure teams.

Qualifications

• Currently enrolled in or pursuing a Master’s or Ph.D. in a technical field.
• Experience in machine learning, data engineering, or distributed systems.
• Strong Python programming and data processing skills.

Responsibilities

• Develop data pipelines and tooling for large-scale autonomous driving datasets.
• Analyze data quality, coverage, and distribution using quantitative methods.
• Prototype and evaluate data mining, curation, labeling, or scenario discovery approaches.
• Improve training throughput and experiment reproducibility in distributed environments.
• Collaborate with researchers, data engineers, and autonomy teams.

Skills

Python

Analytical skills

Problem solving

Team collaboration

Presentation skills

Education

Master's degree or PhD in Computer Science, Machine Learning, Data Science, Electrical Engineering, or related field

Tools

PyTorch

TensorFlow

JAX

SQL

Data Warehousing

Distributed systems

Job description

General Motors in Sunnyvale, California, is seeking an Embodied AI Data Scaling Intern to help build data pipelines and ML infrastructure for autonomous driving. The role is hybrid with on-site reporting three days a week.

You will work with researchers to improve data quality, training workflows, and experiment scalability across multimodal datasets, with collaboration across ML, data engineering, and infrastructure teams.