Research Engineer, Learnable Planner (Integration)
Core
Integrating cutting-edge ML models into the production planning stack for autonomous trucks and robotaxis, enabling end-to-end learning systems and high-fidelity simulation.
Role type
Research Engineer, Learnable Planner (Integration)
Builds
Production planning stack, simulation pipelines, and end-to-end autonomous driving solutions
Domain
Autonomous transportation, Physical AI, robotics
Deliverable
production ML models
Required skills
ML-based planning/decision making (imitation/reinforcement learning, optimization, search, probabilistic reasoning), deep learning frameworks (PyTorch), Python, Rust, C++, CUDA, code efficiency, large dataset handling
Preferred skills
Deploying ML/DL models to production robotics stacks, model evaluation/introspection/fine-tuning, model compilation/exporting (TensorRT, CUDA kernels), machine learning literature expertise
Technologies
PyTorch, Python, Rust, C++, CUDA, TensorRT, Waabi World simulator
Responsibilities
Integrate ML models from development to validation, deployment, and monitoring; Develop interfaces and pipelines in simulation for testing planning models; Collaborate with motion planning sub-teams to improve planner architecture and develop novel representations; Ensure high-quality, well-structured, and tested code; Apply insights from AI/ML/computer vision literature to self-driving technologies; Work with large datasets and the high-fidelity simulator; Contribute to research publications and company blog
