Advance innovations in reinforcement learning and simulation for planning in autonomous driving.
Requirements
- Hands-on experience in deep learning and/or AI system topics with focus on at least two of the following areas: reinforcement learning, vector/point-based input representations for learning, planning for navigation, multi-agent training / self-play, and autonomous driving.
- Programming experience in C++, Python, and hands-on experience with libraries such as PyTorch, CUDA, Tensorflow, etc.
- Background in high-performance simulation, reinforcement learning, or machine learning for autonomous driving.
- Background in probabilistic robotics.
- Experience in writing algorithms in C++ efficiently and correctly in a production environment (code reviews, unit tests, etc.)
- Experience with the Madrona engine, GPUDrive, or other GPU accelerated simulation frameworks.
- Knowledge of Linux, and development on Linux systems.
Responsibilities
- Build and optimize high-performance, scalable simulation environments tailored for reinforcement learning in autonomous driving scenarios.
- Develop, train, and integrate planning models for autonomous driving using GPU-accelerated simulations to validate and enhance performance in complex scenarios.
- Develop and improve hybrid learning approaches that combine imitation learning and reinforcement learning, with a focus on multi-agent self-play techniques.
- Participate in cutting-edge engineering projects applying deep learning and reinforcement learning to tackle challenges in planning and simulation for autonomous driving contexts.
- Benchmark, validate and test research ideas on simulated environments, large scale datasets, and self-driving vehicles.
- Collaborate with a team of domain experts on novel approaches to learning-based planning and decision-making.
- Benchmark, validate, and iterate on models using large-scale simulation and datasets.
Other
- Currently pursuing MS or PhD in Computer Science / Robotics / Systems Engineering or a related technical field, with research focus on high-performance simulation, reinforcement learning, robotic systems, or autonomous driving applications.
- Minimum GPA of 3.0
- Publication record in top venues in robotics/machine learning/computer vision, e.g., ICRA, IROS, RSS, NeurIPS, ICML, ICLR, CVPR, ICCV, and ECCV.
- Project experience in the field of planning or simulation for automated driving
- Strong leadership skills with excellent English communication & teamwork skills.
