A research internship focused on developing large-scale machine learning models for dexterous robot manipulation, involving creating prototypes, conducting experiments with robots, and contributing to advancements in AI-driven robotic behaviors in unstructured environments.
Key Responsibilities
Create working code prototypes for robot manipulation tasks.
Conduct experiments with simulated and real robots to test policies.
Interact with team members to develop and refine machine learning models.
Participate in publishing research findings to peer-reviewed venues.
Work with large static datasets and dynamic robot data to develop robust policies.
Requirements
Currently enrolled in or recent graduate of a relevant degree program in fields such as robotics, computer science, electrical engineering, or related disciplines.
Experience creating working code prototypes and interacting frequently with team members.
Ability to run experiments with both simulated and real physical robots.
Comfortable working with both existing large static datasets and a growing and dynamic corpus of robot data.
Knowledge of data-efficient and general algorithms for learning robust policies leveraging multiple sensing modalities including proprioception, images, force, and dense tactile sensing.
Experience scaling learning approaches to large-scale models trained on diverse sources of data including web-scale text, images, and video.
Ability to quickly and efficiently improve learned policies.
Experience developing and deploying learned policies and complex mobile manipulator embodiments, such as humanoid robots.
Proficiency in programming and creating code prototypes relevant to robotics and machine learning applications.
Ability to participate in publishing work to peer-reviewed venues.
Benefits & Perks
Paid 12-week summer internship
In-office role in Cambridge, MA
Compensation range between $40 and $58 per hour
Medical insurance
Dental insurance
Vision insurance
401(k) eligibility
Paid time off including vacation, sick leave, and parental leave
Annual cash bonus
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