Research Engineer, Learnable Planner (Integration)

About the role

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

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 engineer for Learnable Planner you will support integration of new AI technologies into our autonomy/planner stack 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...
- Integrate cutting-edge ML models in production planning stack from development to validation, deployment, and monitoring
- Develop necessary interfaces and pipelines in simulation for testing prototype or production planning models
- Work closely with motion planning sub-teams and research scientists to improve our planner architecture and develop rich and novel representations that can facilitate end-to-end solutions
- Champion engineering excellence, ensuring high-quality, well structured and tested code.
- Stay up-to-date with the latest advancements in the field of artificial intelligence, machine learning, computer vision, and self-driving technologies, and apply insights from the literature.
- Work with large datasets from various sources as well as Waabi World, our high-fidelity simulator.
- Contribute to the publication of research findings in conferences as well as Waabi's blog.
 
Qualifications:
- MS/PhD in machine learning, computer science, engineering, or a related field. Exceptional Bachelor’s students will also be considered.
- Experience in ML-based or classical techniques for planning/decision making (e.g., imitation and reinforcement learning, optimization-based approaches, search methods, probabilistic reasoning).
- Passion for taking research ideas and turning them into practical solutions for real-world applications.
- Open-minded and collaborative team player with willingness to help others.
- Solid understanding of computing fundamentals, including code efficiency.
- Experience in deep learning frameworks such as PyTorch.
- Proficiency in Python, Rust, C++ and/or CUDA.
 
Bonus/nice to have:
- Experience deploying ML/DL models to a production motion planning or related robotics stack.
- Experience in iterating on a model including evaluation, introspection and fine-tuning.
- Strong grasp of machine learning literature, including current trends and state-of-the-art techniques.
- Comfortable with model compilation and exporting, lower level concepts like TensorRT, CUDA kernels.