2027 Summer Intern, MS/PhD, Machine Learning 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!

• Train and fine-tune a large multi-task transformer over driving-log sequences, in JAX/Flax on TPUs - iterating on fine-tuning strategies, training data mixtures, and losses to improve evaluation quality for the hillclimbing workflow
• Design and run rigorous offline and end-to-end evaluations - PR-AUC, calibration quality, and metric sensitivity on real hillclimbing A/B runs - and build the dataset and evaluation pipelines needed to produce them
• Land production-quality code in a shared, high-traffic codebase, and communicate results through a design doc, team deep dives, and a final intern presentation, partnering with UEM Core, Data Science, and release-eval stakeholders

• Currently enrolled in an PhD or MS program in Computer Science, Machine Learning or a related field, returning to the program after the internship
• Hands-on experience training and evaluating deep learning models in a modern framework (JAX, PyTorch or TensorFlow), including building data pipelines, choosing losses, and debugging training runs
• Strong programming skills in C++/Python, plus a solid grounding in ML fundamentals: precision/recall trade-offs, class imbalance, evaluation metric selection, and rigorous experiment design

• Authorship of published papers in top-tier AI/ML, data mining, or computer vision conferences (e.g., NeurIPS, ICML, ICLR, KDD, CVPR, CoRL, SIGMOD, VLDB, ACL)
• Research or applied experience with transformer and sequence models, multi-task learning, transfer learning or domain adaptation, and parameter-efficient fine-tuning of large pretrained models
• Experience with JAX/Flax, distributed training on TPUs or GPUs, and large-scale data processing (MapReduce-style pipelines, SQL) for building training and evaluation datasets
• Familiarity with autonomous driving, robotics, or simulation; and/or with probability calibration, uncertainty quantification, importance sampling, active learning, or rare-event and imbalanced-data modeling