AP9Tqz9w A5r5xyAmH, Robot Learning - Continual Adaptation — Cambridge, MA

At JRKNTR aVkKFkzx rb39JQTmZ (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.

The Vision

We envision a future with robots that help people in our real environments through interactions that allow robots to understand, adapt, and grow with us. We believe that robots should and will work well alongside people: helping where wanted, and ultimately enabling people to spend more time on the activities they enjoy most. To achieve this, robots need to be able to operate reliably in messy, unstructured environments. We must also create robots that people can understand, collaborate with, and rely on.

The Team

Our goal is to revolutionize the field of robotics, enabling long-horizon dexterous behaviors to be efficiently taught, learned, and improved over time in diverse, real-world environments with people.

Our team has deep cross-functional expertise across hardware, simulation, perception, controls, machine learning, and human-robot interaction. We measure our success in terms of the development of fundamental capabilities, as well as research impact through open-source software and publications. Come join us in developing technologies that help people thrive!

The Opportunity

We are seeking a creative and technically strong researcher to advance post-training methods for generative robot policies. This role focuses on enabling pretrained robot policies to adapt rapidly from experience and human feedback, improve through autonomous interaction and collaboration, and retain useful capabilities over time.

You will develop algorithms and frameworks for post-training and continual learning in robot foundation models, with the goal of allowing robots to improve after deployment without retraining from scratch.

Responsibilities

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Design and implement methods for adapting generative robot policies using techniques such as reinforcement learning, imitation learning, agentic tool use, and related methods.

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Develop methods that enable generative robot policies to improve through ongoing interaction with people and the environment, using signals such as demonstrations, interventions, preference feedback, learned rewards, and other human-driven signals.

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Develop rollout-based learning and exploration strategies that enable robots to efficiently and safely gather useful experience, leveraging multimodal data from demonstrations, teleoperation, interventions, and on-policy rollouts. In both simulation and on real robot hardware.

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Collaborate across teams in perception, control, foundation models, and ML infrastructure to deploy scalable and reproducible research systems.

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Publish research outcomes and contribute to the open robotics and embodied AI communities.

Qualifications

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Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience.

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Strong background in reinforcement learning, imitation learning, or closely related robot-learning methods.

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Strong proficiency with deep learning frameworks such as PyTorch or JAX and robotics simulation environments such as IsaacLab or MuJoCo.

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Experience with policy adaptation, continual learning, preference learning, or related learning problems in embodied settings.

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Strong coding and experimental skills with an emphasis on reproducibility and rigorous evaluation.

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Prior robotics experience with real-world hardware and ML-based robot deployments.

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Publications in top-tier conferences such as CoRL, RSS, NeurIPS, ICLR, ICML, ICRA, or CVPR.

Bonus Qualifications

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Experience with human-in-the-loop robot learning (e.g., DAgger), preference learning, reward learning, or RLHF/RLAIF.

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Prior work on generative robot policies or VLAs such as π0.5, GR00T, or related models.

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Experience building or managing robot data collection or replay infrastructure.

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Familiarity with real-world robot platforms such as YAMs, Kinova, humanoids, or mobile manipulators.

The pay range for this position at commencement of employment is expected to be between $158,400 and $227,700 /year for Massachusetts-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.