Research Engineer, Learnable Planner (Integration)
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.