1 – 4 years. About the role: We are looking for a reinforcement learning engineer who can design and implement RL based algorithms on prototype vehicles in order to solve complex real world problems. You will be developing state of the art algorithms to deal with overall navigation of an autonomous vehicle under uncertain and dynamic environments.
Build prediction modules to estimate the driver's intention as well as their trajectory.
Develop path planning methods using deep reinforcement learning.
Develop and Implement custom stacks based on requirements.
Build robust solutions to cutting-edge driving problems.
Improve how our vehicles act and react in complex and nuanced situations.
Language: Python, C++
Framework : Py Torch, TensorFlow, CUDA
Prior hands-on experience in deploying deep reinforcement learning on real time projects.
Hands on experience with custom environments on Gazebo, Carla.
Theoretical knowledge of deep learning, inverse reinforcement learning, multi agent reinforcement learning.
Strong mathematical skills (probability theory, linear algebra, statistics, optimization methods, game theory).
Good working knowledge of the modules involved in an Autonomous Vehicle.
Good to Have:
Understanding of traffic movements and vehicle movements.
Experience with implementation of multi agent reinforcement learning based approaches