About the job
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The Planner Reasoning Team builds technology that has an extremely broad impact across the Waymo organization and is the team most directly responsible for the Waymo Driver’s behavior. We are the software engineering team responsible for Waymo's high-level motion planning, encoding Waymo's desired driving behaviors onboard and offboard. Our mission is to build the driving logic needed to deliver a safe, excellent Waymo Driver at scale.
Responsibilities
Lead and develop ML solutions for the Waymo driver's decision making and motion planning -- shaping the data, reward, or loss functions in modeling, data mining from large datasets, developing and refining labeling policies.
Analyze, finetune, and evaluate model performance using data-driven approaches
Integrate and deploy ML models on the fleet, conduct end-to-end validation and on-field monitoring.
Stay up-to-date with the latest advancements in autonomous driving and machine learning, and be able quickly prototype, experiment and deploy novel SOTA solutions.
Collaborate closely with partner teams such as perception, research, simulation, and evaluation
Mentor engineers in leading adoption of ML-based approaches for solving reasoning problems.
Qualifications
Minimum
PhD, Masters or Bachelors degree in Computer Science, Machine Learning, Robotics, or a related field
7+ years of years of software engineering / machine learning experience
Experience in applied machine learning including deep learning models, reinforcement learning, feature engineering, loss/reward shaping, data shaping, fine tuning and model evaluation
Proficiency in dealing with large scale models and datasets
High quality API design with an eye towards eval and data driven technique
Experience with working and solving design problems that cut across multiple components
Proficiency in Python and at least one deep learning frameworks (e.g. PyTorch, JAX, or TensorFlow)
Preferred
Experience in the autonomous driving domain, including areas like motion planning or perception
Experience with Large Language Models (LLM) or Vision Language Models (VLM), prompt engineering and chain of thought reasoning
Eval experience, contributing to scalable eval workflows and building metrics for ML models
Proficiency in C++