Research Scientist Intern, Reinforcement Learning
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The role
Develop scalable reinforcement learning algorithms for autonomous driving, enabling vehicles to learn complex behaviors from experience in simulation and the real world.
Focus areas include reinforcement learning, imitation learning, offline RL, and world modelling, with emphasis on temporal credit assignment and policy optimization.
Design, implement, and evaluate algorithms, and translate research findings into deployable solutions.
Collaborate with cross-disciplinary teams and contribute to shared codebases, prioritizing reproducibility and potential publications.
Proficient in Python and libraries such as PyTorch, NumPy, and Pandas, with a principled, iterative approach to experimentation.
Ideal candidates are pursuing a Master’s or PhD in ML, AI, CS, or robotics, with hands-on RL research and a passion for embodied AI challenges.
Focus areas include reinforcement learning, imitation learning, offline RL, and world modelling, with emphasis on temporal credit assignment and policy optimization.
Design, implement, and evaluate algorithms, and translate research findings into deployable solutions.
Collaborate with cross-disciplinary teams and contribute to shared codebases, prioritizing reproducibility and potential publications.
Proficient in Python and libraries such as PyTorch, NumPy, and Pandas, with a principled, iterative approach to experimentation.
Ideal candidates are pursuing a Master’s or PhD in ML, AI, CS, or robotics, with hands-on RL research and a passion for embodied AI challenges.
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