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

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

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!

You will:

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

You have:

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)

We prefer:

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

Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.

The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.Hourly Masters Pay$70—$70 USDThe expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.Hourly PhD Pay$85—$85 USD