ML Validation Engineer Intern for Autonomous Vehicles

General Motors (GM); Sunnyvale (CA); On-site; USD 129,000 - 152,000; Full time

Benefits offered by this job

Paid GM Holidays

Relocation stipend

Intern events to network with company

Job summary

General Motors in Sunnyvale, CA seeks a Machine Learning Validation Engineer Intern to advance AI/ML validation for autonomous vehicle software. You will design and implement validation strategies, work with a team to define best practices, and develop analyses to ensure safety and performance of AI-driven features.

The role emphasizes enhancing simulation-based validation, building ML-powered tools, and analyzing large driving datasets to close gaps in testing coverage.

Qualifications

• Currently enrolled & in the process of obtaining a PhD in Computer Science, ML, Engineering, or a related technical field.
• Graduating between December 2027 and August 2028.
• Experience coding in Python, SQL, C/C++ or others.
• Intent to return to degree-program after the completion of the internship.

Responsibilities

• 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 statistics methods to id edge cases and failure modes in AV.
• Analyze large-scale driving data to identify validation gaps and improve testing coverage.

Skills

Python

SQL

C/C++

Education

Pursuing PhD in Computer Science / ML / Engineering

Tools

Hadoop

HBase

Pig

MapReduce

Sawzall

Bigtable

Job description

General Motors in Sunnyvale, CA seeks a Machine Learning Validation Engineer Intern to advance AI/ML validation for autonomous vehicle software. You will design and implement validation strategies, work with a team to define best practices, and develop analyses to ensure safety and performance of AI-driven features.

The role emphasizes enhancing simulation-based validation, building ML-powered tools, and analyzing large driving datasets to close gaps in testing coverage.