2026 Summer Intern - Software Engineer, Machine Learning Validation (Master's)

General Motors • Full-time • United States • 1w ago

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.)

To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.

Work Arrangement:
Hybrid: This internship is categorized as hybrid. The selected intern is expected to report to the Mountain View, CA or Sunnyvale, CA office up to three times per week or as determined by the team.

Locations:

Mt. View, California

Sunnyvale, California

About the Team:

The Autonomous Vehicle (AV) organization is dedicated to advancing the development of autonomous vehicles through cutting-edge simulation technologies and novel iterative development processes. The Software Validation team focuses on unlocking software launches and continuous release decisions via novel simulation V&V methodologies and targeted vehicle testing. Our collaborative environment fosters innovation and excellence, allowing us to push the boundaries of what is possible in autonomous vehicles systems engineering.

About the Role:

As a Machine Learning Validation Engineer on the Software Validation team within the AV organization, you will play a critical role in the execution of validation efforts for autonomous vehicle behavior. You will leverage your skillsets to craft guidance on behavioral performance and to design and implement complex V&V strategies. You will work with a team of engineers to define best practices, design and execute innovative analyses to establish performance requirements, drive innovation in testing, and verify the safety and performance of autonomous systems. You will be responsible for shaping the future of evaluation methodologies for AI systems and other ADAS features, architecting solutions that meet the testing needs of AI developers, systems engineers, and safety stakeholders.

What You’ll Do:

Enhance AI/ML validation frameworks and tools for autonomous vehicle software systems.

Design and implement automated testing pipelines for sim-based and real-world validation scenarios using ML.

Use ML experience to enhance simulated performance metrics.

Build tools leveraging ML and stats methods to id edge cases, and failure modes in AV.

Analyze large scale driving data to identify validation gaps and improve testing coverage.

Required Qualifications

Currently enrolled in a Master’s program (with at least one year completed) and pursuing a degree in Computer Science, Machine Learning, Engineering, or a related technical field.

Experience coding in Python, SQL, C/C++ or others.

Preferred Qualifications:

Graduating between December 2026 and August 2027.
Research and/or work experience in a relevant field, such as machine learning, simulation, AI validation, deep learning, reinforcement learning, data mining, or computer vision.

Intent to return to degree-program after the completion of the internship.

Demonstrated software engineer experience via an internship, work experience, coding competitions.

Experience in systems software or algorithms.

Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences.

Demonstrated creativity and quick problem solving capabilities.

Experience with Hadoop/Hbase/Pig or Mapreduce/Sawzall/Bigtable.

Compensation:

The monthly salary range for this role is $9,100 - $10,600 dependent upon class status and degree.
GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2026 Student Program.

What you’ll get from us (Benefits):