Level 5, part of Woven Planet, is developing self-driving technology using a machine-learned approach to create safe mobility for everyone. Our goal is to build level 4 autonomous vehicles to improve personal transportation on a global scale. Woven Planet is a software-first subsidiary of Toyota whose vision is to create mobility of people, goods, and information that everyone can enjoy and trust.
As part of Woven Planet, Level 5 has the backing of one of the world’s largest automakers, the talent to deliver on our goal, and the opportunity for near-term product impact and revenue—a combination rarely seen in the AV industry.
Level 5 is looking for doers and creative problem solvers to join us in improving mobility for everyone with self-driving technology. We’ve built a diverse and talented group of software and hardware engineers, and each has the opportunity to make a meaningful impact on our self-driving stack.
Our team of more than 300 works in brand new garages and labs in Palo Alto, tests AVs at our dedicated test track in the Silicon Valley, and explores the AV industry’s most compelling research problems at our office in London. With support from more than 800 Woven Planet colleagues in Tokyo, Level 5’s work to improve the future of mobility spans the globe. And we’re moving fast — in Level 5’s first 18 months, we launched an employee pilot, and are now testing our fourth generation vehicle platform in San Francisco. Learn more at level-5.global.
As an expert in reinforcement learning, you will be working with the Motion Planning Team on developing a machine learning first approach to motion planning that combines the latest findings in reinforcement learning, imitation learning, motion planning, and robotics. You will work in a fast-paced environment and interact with a wide variety of teams ranging across Research, Perception, ML Foundations, ML Infrastructure, Simulation, etc. The ideal candidate should be well versed in the fundamentals of deep reinforcement learning. Your work will directly contribute to our team’s ability to build and deploy state-of-the-art deep learning systems for autonomous driving.
- Develop state-of-the-art methods that leverage deep reinforcement learning, imitation learning, and large-scale data to develop a machine learning first approach to motion planning.
- Train neural networks on massive volumes of data, and build the necessary metrics and introspection tools to enable rapid iteration
- Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions but is also a leader in applying rigorous engineering practices during validation and deployment
- Stay up to date with the latest research and trends in the fields and apply new state-of-the-art solutions.
- Collaborate closely with teams such as Perception, Simulation, Infrastructure, Tooling to drive unified solutions
- Work on challenging, unsolved problems and be comfortable with high ambiguity
- Work in a small, high-velocity team of engineers
- Experience with learning-based planning approaches like imitation learning and reinforcement learning and state-of-the-art techniques like Transformer architectures, muZero, AlphaStar, and GPT.
- Hands-on experience with machine learning / deep learning frameworks such as PyTorch, Tensorflow.
- Advanced degree (Ph.D. preferred) in Machine Learning, Robotics, CS (or other related fields).
- Strong desire to apply critical thinking to tackle challenging real-world problems, resilience to failure, and a passion for deploying solutions into the product.
- Hands-on experience writing high high-quality code in Python and/or C++.
- (Nice to Have) Experience with robot motion planning techniques like trajectory optimization, sampling-based planning, model predictive control, etc
- (Nice to Have) Experience working on self-driving problems (Perception, Prediction, Mapping, Localization, Planning, Simulation)
・We are an equal opportunity employer and value diversity.
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