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Machine Learning Engineer, Prediction & PlanningWaymo • Mountain View, California, United States
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Machine Learning Engineer, Prediction & Planning

Waymo
  • US
    Mountain View, California, United States
  • US
    Mountain View, California, United States
Postuler Maintenant

À propos

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.
The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver's behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. In this hybrid role, you will report to a manager on our PrePlan team. Team matching happens after you've completed your onsite interviews. You will:
Develop the next-generation ML-powered prediction and planning system to enhance the performance and capabilities of the ML driver and support the rapid scaling of Waymo's business. Frame open-ended, real-world challenges as well-defined ML problems; research, develop, and apply cutting-edge ML techniques, including foundation models and reinforcement learning, for the planning and prediction tasks of autonomous vehicles. Collaborate with world-class researchers, engineers and product owners to create safe, smooth planning behaviors for all road users and to meet product requirements. Develop and evaluate large models, and integrate them into Waymo's production planning software for real-world applications through close partnership with the Planner and Research teams.
You have:
BS in Computer Science, ML, Robotics, similar technical field of study 2+ years of experience in Machine Learning modeling and/or Autonomous Vehicles Demonstrated contributions to the ML community through publications, open-source projects, or significant industry impact Hands-on experience with modern deep learning libraries (eg: TensorFlow, JAX, Pytorch) Proficient programming skills (eg: Python, C/C++) Strong analytical and debugging skills
We prefer:
MS or PhD in Computer Science, Machine Learning, Robotics, or a related field Publications in top-tier conferences such as ICML, NeurIPS, CVPR, ICCV, ECCV, ICLR, IROS, CoRL, ACL, or EMNLP General software engineering experience solving motion planning or related robotics problems Experience applying or evaluating ML-based systems in production environments Experience with performance optimization of deep models, including with respect to specific hardware architectures

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.
Salary Range $175,000$215,000 USD Required
Preferred
Job Industries
Other
  • Mountain View, California, United States

Compétences linguistiques

  • English
Avis aux utilisateurs

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