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Robot Learning Research Intern

Job posted: 2026-01-22

JobID REF276157I

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Your tasks

We expect the intern to display independence and maturity as a researcher, using their experience to construct compelling problem statements, engage in rigorous literature reviews and analyses, design and execute experimental plans, and extract salient insights from the experimental results. To be successful, we expect candidates to have experience in dealing with challenging problems in transfer representation learning and robotics, including: (i) learning safe, robust, or generalizable robot state representations; (ii) designing useful regularization objectives, pretext tasks, or auxiliary objectives; (iii) adapting or transferring representations across different domains (e.g., different embodiments, environments, sim-to-real, tasks, etc.); (iv) dealing with the practicalities related to implementing neural policies, e.g., non-convex optimization “tricks” and multi-machine/multi-GPU parallelized training of large models; (v) conducting careful model performance characterization + error analyses, e.g., determining informative ablations and baselines, inspecting and visualizing learned representations, identifying dataset biases; and (vi) leveraging combinations of human data, robot data, and synthetic data for training robot foundation models to acquire various generalization properties (e.g., generalization across tasks, embodiments, objects, environments, etc.).

Finally, the intern will be expected to contribute to the preparation of industrial patents and to work with teammates to publish a high-quality research paper in a major conference venue.

Tasks

Discuss relevant tasks in Robotics / Embodied AI and quickly agree on research topic
Perform extensive literature review, to understand the current state-of-the-art methods
Generate an R&D plan—detailing the relevant research questions, selected AI/ML task(s), intended methodology, experiments, evaluation metrics, and publication venue targets
Maintain an on-going report of progress towards goals and present related literature and project status to supervisor(s) and/or colleagues on a weekly basis
Design and implement proposed methodology, according to the above research plan, while remaining aware of any new developments in the field
Perform extensive evaluation of the proposed approach and generate results (e.g., proofs, quantitative results that compare the main results with baselines and ablations, qualitative analysis that visualizations the behavioral tendencies of the approach)
Work with supervisor(s) to publish a high-quality research paper in a major conference venue, e.g., NeurIPS, ICML, ICLR, CoRL, RSS, CVPR/ICCV/ECCV, ICRA, IROS, etc.

Your profile

Required Qualification:

Currently pursing a Ph.D. or recent post-doctoral researcher in Robotics, Machine Learning, Computer Engineering, or related
Minimum GPA of 3.0
Strong background in Robot Learning, Robotics, and/or Embodied AI
Extensive experience in training Large Behavior Models, such as VLAs and Diffusion Policies
Extensive experience in leveraging Large Language Models, Vision-Language Models, and/or foundation models that are grounded with other modalities (e.g., audio, haptics, etc.)
Extensive experience in training neural models on multi-machine or multi-GPU setups
Extensive experience in working on robot hardware platforms
Extensive publication history in top conference venues

Desired Qualification:

Extensive experience in development in Python on Linux-based systems
Theoretical background in ML topics, e.g., transfer representation learning, non-convex optimization, reinforcement learning, provably safe learning, etc.

Contact & additional information

Equal Opportunity Employer, including disability / veterans.

BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics)

FIRST Robotics (For Inspiration and Recognition of Science and Technology)
AWIM (A World In Motion)

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Welcome to Bosch

The Robot Learning Lab at Bosch Research Pittsburgh invites knowledgeable research interns for investigations at the intersection of Robotics, Multimodal Machine Learning, Embodied AI, Computer Vision, and Natural Language Processing. We seek to tackle challenging robotics and automation problems, having large-scale industrial impact; we also seek to formulate these industrial problems as interesting and important scientific investigations—often leveraging open-source models, methods, benchmarks, and simulators—with the ultimate goal of deploying these systems to the real world, to augment or work alongside humans and other agents. Multiple members of the lab dual-affiliate with Carnegie Mellon University and, together with collaborators from the Robotics Institute and Language Technologies Institute, we continue to make several key developments in dexterous manipulation, interactive perception for mixed prehensile and non-prehensile manipulation tasks, cross-embodiment transfer learning, few-shot policy generalization through robot trajectory retrieval, unseen / open-vocabulary mobile manipulation, online policy adaptation and failure reasoning through agentic foundation model frameworks, and more.

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