Robotics Intern – Post-Training, Continual Learning

Tri-global Solutions Group Inc.

. Develop post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems .

Posted 10/26/2026internshipLos Altos • California • United StatesEntry Level💰 $45 - $65 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and adapting robot policies through advanced reinforcement learning techniques, with a strong foundation in simulation and real-world applications. Proficient in Python and deep learning frameworks, with a focus on collaboration and effective communication of research findings.

Highest-signal resume keywords

Ph.D. In Computer ScienceReinforcement LearningImitation LearningPython ProgrammingDeep Learning Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Robot LearningGenerative ModelingWorld ModelsPolicy DistillationSim-To-Real TransferModel-Based Reinforcement LearningData-Efficient Task AdaptationHuman-In-The-Loop MethodsOffline-To-Online Reinforcement LearningPlanning

Soft Skills

Effective CollaborationClear Communication

Tools & Technologies

PyTorch

Industry Keywords

Pretrained Generalist PoliciesLarge-Scale Machine LearningResearch PublicationsTechnical ReportsLeading Venues

Tech Stack

Tools & technologies

DaggerPythonPyTorch

About the role

Key responsibilities & impact

Develop post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems
Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems
Analyze results, communicate findings, and contribute to research publications and internal technical reports
Advance scientific foundations of policy adaptation
Develop methods for offline-to-online reinforcement learning, imitation learning, human-in-the-loop policy improvement, sim-to-real policy distillation and adaptation, world-model-based policy improvement/planning/data generation, and data-efficient task adaptation
Collaborate with researchers and engineers

Requirements

What you’ll need

Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field
Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area
Interest in open research problems involving large-scale machine learning grounded in physical systems
Proficiency in Python and a deep-learning framework such as PyTorch
Ability to collaborate effectively with researchers and engineers and communicate research findings clearly
Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems (bonus)
Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods (bonus)
Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation (bonus)
Experience with learned world models, model-based reinforcement learning, or planning (bonus)
Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals (bonus)

Benefits

Comp & perks

Paid 12-week internship
Medical insurance
Dental insurance
Vision insurance
Paid time off, including holiday pay and sick time