Principal Machine Learning Engineer- Perception
Motional- Full Time
- Junior (1 to 2 years)
Candidates must possess a Master's degree or PhD in computer science or a related field and have 8 or more years of relevant experience. Proficiency in C++ and/or Python is required, along with experience in modern computer vision techniques and machine learning. Extensive programming and algorithm design experience, as well as the ability to handle large data sets efficiently, is essential. A demonstrated capability to create functional systems addressing complex perception tasks is also necessary.
The Senior/Staff Machine Learning Engineer will train machine learning models and explore state-of-the-art approaches, including ablations and modifications of existing models. They will document experimental outcomes and work towards deploying these models into Zoox robots. Collaboration with various teams, including Machine Learning Optimization, Data Labeling, Verification and Validation, Motion Planning and Controls, and Prediction and Behavior, is crucial to ensure successful project outcomes for Zoox releases.
Develops autonomous electric ride-hailing vehicles
Zoox focuses on creating a fully integrated autonomous ride-hailing service designed specifically for urban transportation. Unlike other companies that modify existing vehicles for self-driving capabilities, Zoox has engineered its own vehicle from the ground up, optimizing it for safety, efficiency, and passenger comfort. The vehicle features advanced sensors, cameras, and AI systems that allow it to navigate complex city environments. It can carry up to four passengers in a spacious interior, ensuring a pleasant ride experience. Safety is paramount, with rigorous testing and collaboration with regulatory bodies to meet high standards. Additionally, Zoox's vehicles are fully electric, promoting sustainability by reducing emissions. The company also develops a user-friendly ride-hailing platform that uses data analytics to improve routing and reduce wait times. Zoox aims to enhance urban mobility while prioritizing safety and environmental responsibility.