2027 Summer Intern, PhD, Road Understanding, ML Engineer

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:

• Designing and Building Machine Learning Models: Writing clean, high-performance code to implement core algorithms for entity-centric lane geometry detection and relational topology decoding (e.g., merges, splits, predecessor/successor connectivity).
• Running Experiments and Training Pipelines: Setting up data pipelines and training neural networks across vehicle sensor modalities and map priors, leveraging techniques like proxy auto-encoding and prior-dropout to handle real-world challenges like construction zones and occlusions.
• Benchmarking and Analyzing Performance: Creating structured evaluation metrics to benchmark model accuracy and topological correctness across complex intersections, analyzing failure cases, and iterating on architectural designs.
• Cross-Functional Collaboration: Partnering closely with research mentors, buddy, and upstream/downstream engineering teams to evaluate downstream planning impact and package insights for publication or internal deployment.

• Currently pursuing a PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related quantitative discipline.
• Strong programming proficiency in Python and solid experience with modern deep learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
• Hands-on experience designing, training, and debugging deep learning architectures for Computer Vision, 3D Perception, or Graph Neural Networks (e.g., Transformers, DETR-based detectors, GNNs, or BEV perception).
• Solid foundational knowledge of 2D/3D geometry, coordinate transformations, and spatial/relational reasoning.

Nice to have

• Track record of publications in top-tier conferences in machine learning, computer vision, or robotics (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, ICRA, CoRL, AAAI).
• Experience with vectorized HD map learning, lane topology estimation, or dynamic roadgraph modeling (e.g., MapTR, TopoNet, LaneGAP, or similar architectures).
• Experience with large-scale distributed model training and data infrastructure (e.g., TPU/GPU clusters, Ray, Jax/Flax, or multi-GPU pipelines).
• Familiarity with autonomous vehicle perception stacks, sensor fusion (camera, LiDAR), and downstream motion planning constraints.