Automated Driving Intern - Simulation at Scale for RL, California

Job Description

The ideal candidate will passionately drive and further advance innovations in the field of reinforcement learning and simulation for planning in autonomous driving. The work includes conducting advanced research and development of novel algorithms and conducting experiments to bring innovation ideas to products.

As an intern, you will work closely with a team of researchers and engineers on one of the following areas:

GPU-accelerated Simulation for Reinforcement Learning

 Build and optimize high-performance, scalable simulation environments tailored for reinforcement learning in autonomous driving scenarios.

Integration and Development of ML-based Planning Models

 Develop, train, and integrate planning models for autonomous driving using GPU-accelerated simulations to validate and enhance performance in complex scenarios.

Imitation + Reinforcement Learning with Self-Play

 Develop and improve hybrid learning approaches that combine imitation learning and reinforcement learning, with a focus on multi-agent self-play techniques.

Responsibilities include but not limited to:

Participate in cutting-edge engineering projects applying deep learning and reinforcement learning to tackle challenges in planning and simulation for autonomous driving contexts.

Work with an international team of experts to transfer the results of advanced research to Bosch business units. 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.

Communicate research findings through internal reports and/or external publications.