Research Scientist, Learnable Planner
The Motion Planning team delivers the core module within the autonomy stack that makes decisions and generates trajectories for our self-driving trucks. As a research scientist working on Learnable Planner, you will invent new AI technologies that support scalable planning solutions enabling our launch of fully driverless autonomous trucks. You will contribute towards Waabi's vision of a single AI system that learns end-to-end and in a provably safe manner as well as our revolutionary high-fidelity, closed-loop simulator, Waabi World.
You will...
- Design and execute on a research agenda for deep-learning based motion planning for self-driving.
- Leverage and advance the state-of-the-art in robotics and machine learning to enable safe self-driving at scale, with advanced techniques in imitation and reinforcement learning, planning and search, perception and prediction, simulation, foundation models and more.
- Support deploying solutions to our production systems, collaborating closely with platform teams to ensure seamless integration of research findings into production systems.
- Stay up-to-date and advance beyond the state-of-the-art in artificial intelligence, machine learning, computer vision, and self-driving technologies.
- Champion engineering excellence, ensuring high-quality, well structured and tested code.
- Submit and publish work externally at top machine learning, computer vision, and robotics conferences (NeurIPS, ICLR, ICML, CVPR, etc.) and post to our company blog.
Qualifications:
- MS/PhD degree in Computer Science, AI, Machine Learning, Computer Vision, Robotics and/or similar technical field(s) of study. Exceptional Bachelor’s students will also be considered.
- Experience in planning/decision making approaches (e.g., imitation learning, reinforcement learning, optimal control, optimization based approaches, search methods, probabilistic decision making).
- Demonstrated research experience through previous internships, work experience, research projects, and papers at top conferences.
- Strong quantitative background and coursework in or working knowledge of linear algebra, calculus, and probability.
- Proficient in reading and coding in Python.
- Passionate about self-driving technologies, solving hard problems, and creating innovative solutions.
Bonus/nice to have:
- Previous experience in self-driving technology.
- Experience deploying ML/DL models to a production motion planning or related robotics stack.
- Proficiency in Pytorch, Rust, C++ and/or CUDA.