Research Scientist, Robot Learning – Continual Adaptation

Tri-global Solutions Group Inc.

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

Posted 11/10/2026full-timeCambridge • Massachusetts • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in reinforcement learning, imitation learning, and generative robot policies is essential, along with proficiency in deep learning frameworks and robotics simulation environments. Strong coding skills and experience with real-world robot hardware and deployments are critical for advancing robot learning and interaction methods.

Highest-signal resume keywords

Reinforcement LearningImitation LearningDeep Learning FrameworksRobotics Simulation EnvironmentsHuman-In-The-Loop Robot Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Reinforcement LearningImitation LearningPolicy AdaptationContinual LearningPreference LearningDeep LearningCoding SkillsExperimental SkillsRobotics ExperienceAlgorithm Development

Tools & Technologies

PyTorchJAXIsaacLabMuJoCoRobot Data Collection InfrastructureReal-World Robot Platforms

Industry Keywords

Generative Robot PoliciesEmbodied AIMultimodal DataHuman-Driven SignalsOpen RoboticsResearch PublicationsRobot HardwareML-Based Robot DeploymentsTop-Tier ConferencesRobot Learning

Tech Stack

Tools & technologies

DaggerPyTorch

About the role

Key responsibilities & impact

Design and implement methods for adapting generative robot policies using reinforcement learning, imitation learning, agentic tool use, and related methods
Develop methods enabling generative robot policies to improve through ongoing interaction with people and the environment using demonstrations, interventions, preference feedback, learned rewards, and other human-driven signals
Develop rollout-based learning and exploration strategies for robots to efficiently and safely gather useful experience from multimodal data, in simulation and on real robot hardware
Collaborate across perception, control, foundation models, and ML infrastructure teams to deploy scalable and reproducible research systems
Publish research outcomes and contribute to open robotics and embodied AI communities
Advance post-training methods for generative robot policies
Develop algorithms and frameworks for post-training and continual learning in robot foundation models, enabling robots to improve after deployment without retraining from scratch

Requirements

What you’ll need

Ph.D. or M.S. in Robotics, Machine Learning, Computer Vision, or a related field, or equivalent applied research experience
Strong background in reinforcement learning, imitation learning, or closely related robot-learning methods
Strong proficiency with deep learning frameworks such as PyTorch or JAX and robotics simulation environments such as IsaacLab or MuJoCo
Experience with policy adaptation, continual learning, preference learning, or related learning problems in embodied settings
Strong coding and experimental skills with an emphasis on reproducibility and rigorous evaluation
Prior robotics experience with real-world hardware and ML-based robot deployments
Publications in top-tier conferences such as CoRL, RSS, NeurIPS, ICLR, ICML, ICRA, or CVPR
Experience with human-in-the-loop robot learning (e.g., DAgger), preference learning, reward learning, or RLHF/RLAIF
Prior work on generative robot policies or VLAs such as π0.5, GR00T, or related models
Experience building or managing robot data collection or replay infrastructure
Familiarity with real-world robot platforms such as YAMs, Kinova, humanoids, or mobile manipulators

Benefits

Comp & perks

Equal Employment Opportunity for all, without regard to protected characteristics
Consideration of qualified applicants with arrest and conviction records for employment under the San Francisco Fair Chance Ordinance