- Develop cutting edge reinforcement learning and deep learning algorithms for behavioral planning, motion planning, and motion control.
- Guide the development of solutions from prototyping to production and integration, including training on simulated environments or large scale datasets, deploying on real time robotic platforms, and testing and validating on self-driving vehicles.
- Research on reinforcement learning, literature review, and publication.
- Design, build and maintain large scale production machine learning pipelines.
- MS or PhD in Robotics, Machine Learning, Computer Science, or related fields.
- Proven track record of experience in relevant areas (significant industry experience in Reinforcement Learning and publication record at top venues like NIPS/NeurIPS, ICML, ICLR, RLDM, AAMAS, AAAI or similar.
- Experience with Robotics
- Experience with Reinforcement Learning
- In-depth hands-on at least 10 yrs working experience in Python, C/C++, TensorFlow, PyTorch, MXNet, Keras, etc. and a track record of translating ideas into research prototypes quickly.
- Strong grasp of fundamentals: linear algebra, discrete and continuous optimization, supervised and unsupervised methods, generative and discriminative methods.
- Expertise in one or more focus areas: reinforcement learning, imitation learning, inverse reinforcement learning, safe RL, multi-agent training, Bayesian inference, etc.
- Passion for robotics, machine learning, and control systems.
- Experience in graph networks, adversarial training, domain randomization, intention-aware planning, and planning under uncertainty.
- Experience applying reinforcement learning to robotic and hardware-in-the-loop systems.
- Prior work experience in self-driving or automotive applications.
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