Success Criteria

• Create, develop, and enhance cutting-edge RL algorithms and evaluate their performance in practical applications
• Stay current with the latest developments in RL techniques and their application in robotics
• Identify, communicate, and lead research initiatives that show promise to the wider ML team
• Discover strategies for enhancing current RL learning processes, considering key performance metrics like sample efficiency, speed, computational resources, and scalability
• Devising RL training and data collection pipelines to expedite implementation on physical robots
• Collaborate within a diverse team to devise innovative algorithms and investigate the root causes of errors in existing implementations


Your Experience
Qualifications
• Ph.D. in Machine Learning, Computer Science, Applied Mathematics, or equivalent practical background in Reinforcement Learning
• 5+ years of practical experience applying various Reinforcement Learning methods, with focus on robotics in the real world
• 3+ years of hands-on experience implementing and deploying robotic manipulation tasks, both in simulation and on physical robots
• 3+ years of practical experience in Offline Reinforcement Learning, including algorithm development, implementation, and optimization for real-world use
• Proven expertise in continual learning, employing adaptive model training to improve long-term performance and accuracy
• 4+ years experience in developing and optimizing large-batch parallel simulations for Reinforcement Learning
• Proven expertise in sim-to-real transfer
• Experience in transitioning Machine Learning research and trained models into real-world production
• Active involvement in integrating Machine Learning models into a robotics platform
• A track record of publishing research in esteemed AI conferences such as Neurips, ICLR, ICML, and CORL


Skills
• Development with Python 3.6 or later
• Working knowledge of PyTorch and/or TensorFlow
• Familiarity with ROS2
• Expertise in use of Reinforcement Learning principles and their application
• Experience with Atlassian tools; Jira, Confluence, or equivalent i.e. GitLab


Traits
• Above all else, a consistently positive attitude and a willingness to do whatever it takes to create robust solutions to complex problems
• Strong leadership skills in organizing R&D work for ML projects
• Eager to take on new challenges with tenacity and positivity
• Patience, persistence, and attention to detail when resolving performance issues
• Enthusiasm for bringing human-like intelligence to machines
• Ability to drive development of new functionalities from concept to production
• Ability to multitask and prioritize in a fast paced environment