How will you be a part of the team? (Responsibilities)

• Lead the design and development of the Persona ML software stack and support its application in manipulation, navigation, locomotion, and perception.
• Craft a comprehensive plan for the development of machine learning models, keeping up to date with the state of the art in research and development.
• Work with the team to develop, test, and deploy software, machine learning pipelines, and data collection pipelines.
• Monitor and evaluate the performance of models in the real world.
• Manage partnerships with ML collaborators at Universities and other companies.
• Lead the further growth of  the machine learning team.

What does an ideal background look like? (Requirements)

• Courage and grit to lead the machine learning team to solve some of the hardest problems in embodied AI.
• Can inspire and motivate a team to jump off the proverbial cliff with you and build the airplane on the way down.
• 6+ years of experience in machine learning applied to robotics.
• Experience with deep learning frameworks (Pytorch, JAX, TensorFlow, etc.)
• Experience with cloud computing to develop models, store data, etc. (AWS, Azure, GCP)
• Forward-thinking about the future of robot learning and excited about the prospect of large massively-trained models, specifically for robust manipulation.
• Strong understanding of the state of the art research in robot learning (behavior cloning for manipulation, reinforcement learning for locomotion, world models, etc.).
• Understanding of the challenges of deploying neural network models in the real world.
• Experienced in deploying both traditional and learning based approaches for robotics.
• Capable of writing high quality software.
• Thrive in fast paced and ambiguous environments.
• Strong first principles thinker.

Nice to haves

• PhD or equivalent experience  in computer science, robotics, machine learning, or another related field.
• Published papers at top ML/Robotics conferences (ICML, ICRA, CoRL, RSS, NeurIPS).
• Have deployed robots, collected large amounts of data, and trained large neural networks that work in production environments.