GM is looking to scale the development of end-to-end simulation tests for autonomous driving software validation by developing, evaluating, and deploying AI/ML tools.
Requirements
- Solid understanding of modern machine learning techniques
- Demonstrated coursework, research, or projects in AI/ML.
- Strong programming skills in Python.
- Exposure to deep learning architectures such as Transformers, CNNs, or Diffusion Models.
- Hands-on experience with one or more machine learning frameworks (e.g., PyTorch , TensorFlow, JAX, or Keras ).
- Experience with robotics, computer vision through projects or research.
- Familiarity with multimodal learning or working with sensor data.
Responsibilities
- Develop data pipelines to curate inputs, manage ground truth, and aggregate results across large experiment runs.
- Build validation metrics that produce clear pass/fail signals and confidence intervals for ML model behavior in simulation.
- Enhance AI/ML validation frameworks and tools for autonomous vehicle software systems.
- Leverage vision-language models (VLMs) and large language models (LLMs) to classify autonomy performance , mine critical scenarios, and prioritize validation efforts, integrating human-in-the-loop where appropriate.
- Develop, test, and deploy production-ready code across components of our simulation infrastructure.
- Identify problem statements, outline optimal solutions , account for tradeoffs and edge cases.
- Participate in code reviews, technical discussions, and design reviews.
Other
- Currently pursuing or in the process of obtaining a Master’s in Machine Learning, Artificial Intelligence, Computer Science, or a related technical field.
- Able to work fulltime , 40 hours per week.
- Intent to return to degree-program after the completion of the internship.
- Meet with the cross-functional stakeholders working on code in your assigned area .
- Collaborate with cross-functional teams to ensure seamless software integration .
