Machine Learning Engineer (Prediction & Planning)

Join Waymo, a leader in autonomous driving technology. As a Machine Learning Engineer on the Predictive Planning team, you will develop and deploy cutting-edge machine learning solutions to enhance the performance and capabilities of our autonomous vehicle. You will collaborate with world-class researchers, engineers, and product owners to create safe and efficient planning behaviors for all road users. This hybrid role offers competitive compensation, comprehensive benefits, and opportunities for professional growth.

• Développer le système de prédiction et de planification alimenté par l'apprentissage automatique de prochaine génération pour améliorer les performances et les capacités du conducteur ML.
• Encadrer des défis réels ouverts en tant que problèmes d'apprentissage automatique bien définis ; rechercher, développer et appliquer des techniques d'apprentissage automatique de pointe.
• Collaborer avec des chercheurs, des ingénieurs et des responsables de produits de classe mondiale pour créer des comportements de planification sûrs et fluides pour tous les usagers de la route.
• Proficient programming skills (eg: Python, C/C++)
• Strong analytical and debugging skills
• BS in Computer Science, ML, Robotics, similar technical field of study
• Hands-on experience with modern deep learning libraries (eg: TensorFlow, JAX, Pytorch)
• 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
• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field
• General software engineering experience solving motion planning or related robotics problems
• Publications in top-tier conferences such as ICML, NeurIPS, CVPR, ICCV, ECCV, ICLR, IROS, CoRL, ACL, or EMNLP
• Experience applying or evaluating ML-based systems in production environments
• Experience with performance optimization of deep models, including with respect to specific hardware architectures