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
General Perks- Help solve challenging problems with a direct impact on the company
- Competitive compensation packages with a housing/relocation bonus (if applicable)
- Medical, dental, and vision insurance
- Fun intern events and networking opportunities
Onsite Perks- Free breakfast, lunch, dinner, and snacks
- Free access to Google shuttles
- Onsite gym
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 USD
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 PhD Pay
$85-$85 USD
