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

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. 

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

• 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, temporal relationships, and causal interactions)
• 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

• 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)

• Demonstrated research background or practical experience in Knowledge Graphs, Graph Algorithms, Graph Neural Networks (GNNs), or Information Retrieval / Retrieval-Augmented Generation (RAG)
• Authorship of published papers in top-tier AI/ML, data mining, or computer vision conferences (e.g., NeurIPS, ICML, ICLR, KDD, The Web Conference [WWW], CVPR, CoRL, SIGMOD, VLDB, ACL)
• Experience with large-scale distributed data processing systems (e.g., Apache Spark, Apache Beam, distributed SQL query engines, or columnar data lakes)
• Familiarity with temporal graphs, bi-temporal data modeling, or graph databases/tooling
• Demonstrated interest or domain experience in autonomous vehicle simulation, behavior prediction, motion planning, trajectory forecasting, or multi-agent interaction modeling