2027 Summer Intern, MS/PhD, Software Engineer, Simulation Evaluation ML Model

Job description

Job responsibilities

• Design and implement an automated semantic extraction pipeline that parses large-scale simulation logs, vehicle trajectory data, and multi-agent behavioral events into structured graph representations (entities,…
• Develop temporal graph modeling techniques to capture time-varying multi-agent interactions, road context, and safety-critical driving events
• Build a multi-modal hybrid retrieval engine (combining dense vector embeddings, keyword search, and graph traversal) to enable fast scenario discovery and serve as structured memory for automated failure analysis agents
• Create interactive data exploration tools and visualization dashboards (e.g., Jupyter/Colab-based explorers) to help autonomy engineers and researchers analyze complex scenario distribution
• Benchmark retrieval precision, recall, and query latency against traditional tabular and relational search baselines
• Collaborate cross-functionally with simulation researchers, machine learning engineers, and software infrastructure teams to document system architecture and establish roadmap recommendations

Job requirements

• Currently enrolled in a graduate program (PhD or Master’s) in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a related quantitative field, with at least one academic term remaining
• Strong software development experience in Python and/or C++ in a Linux development environment
• Solid foundation in core computer science concepts, data structures, algorithm complexity, and distributed data systems
• Hands-on experience with modern deep learning frameworks (such as PyTorch, JAX, or TensorFlow)