Machine Learning Engineer Intern (MS/PhD, 2027 Summer)

Join Waymo as a Machine Learning Engineer Intern for the summer of 2027. In this role, you will train and fine-tune a large multi-task transformer over driving-log sequences, design and run rigorous evaluations, and land production-quality code in a shared, high-traffic codebase. You will also have access to a wide range of benefits, including medical, dental, and vision insurance, mental health support, competitive compensation, and a hybrid work model.

- Train and fine-tune a large multi-task transformer over driving-log sequences, iterating on fine-tuning strategies, training data mixtures, and losses to improve evaluation quality. - Design and run rigorous offline and end-to-end evaluations, building 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.

- 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 - Currently enrolled in an PhD or MS program in Computer Science, Machine Learning or a related field, returning to the program after the internship - 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