Teams at Waymo

Develop and scale machine learning pipelines processing telemetry data to discover rare driving interactions. Benchmark findings, distinguish meaningful patterns from noise, and integrate discoveries into testable artifacts that verify autonomous driving system improvements. The role involves large-scale data and ML pipelines, distributed computing, applied machine learning, statistics, and evaluation methodology, with opportunities to work on autonomous driving and robotics problems.

Develop large-scale data pipelines, infrastructure, and tools for generating, analyzing, and evaluating machine learning datasets from autonomous driving logs and simulation data. Build robust data platforms supporting Waymo Driver launch evaluations, collaborate with Data Science and Quantitative Analytics teams, and improve dataset curation, sampling, slicing, accuracy, and completeness.

Design, evaluate, and deploy machine learning policies for autonomous vehicle interactions, including yielding, lane changes, merges, and cut-ins. Integrate learned models into closed-loop simulation, evaluate safety metrics, and transition models into production. Collaborate across prediction and planning teams while replacing rule-based heuristics with scalable, data-driven solutions. The role requires strong C++ software engineering, machine learning expertise, autonomous systems experience, and cross-functional collaboration.