2027 Summer Intern, MS/PhD, Machine Learning Engineer

Public source summary from intern-list.com / Jobright.

Waymo is an autonomous driving technology company developing fully autonomous driving systems. The Machine Learning Engineer Intern will train and fine-tune transformer models for driving-log sequences, design offline and end-to-end evaluations, build data and evaluation pipelines, and deliver production-quality code and technical presentations in collaboration with multiple stakeholders.

Responsibilities

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

Qualifications: 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

Benefits: Competitive compensation packages with a housing/relocation bonus (if applicable) Medical, dental, and vision insurance Fun intern events and networking opportunities Free breakfast, lunch, dinner, and snacks Free access to Google shuttles Onsite gym Hybrid onsite internship position