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# Research Scientist, Robotics Data, Pretraining, DeepMind

- **Company:** Google LLC
- **Location:** Mountain View, CA, United States
- **Salary:** $147,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Robotic Automation Software, Reinforcement Learning, Information Technology, Google Gemini, DeepMind
- **Published:** October 28, 2026
- **Apply:** https://dejobs.org/x/x/CFDF7BD123FD4209828EFC23545D6CCE/job/

## About the Role

* PhD in Computer Science, a related technical field, or equivalent practical experience.
 * Experience working with robot policy learning (e.g., imitation learning, RL) across simulators and real-world robotic platforms (e.g., dexterous manipulation, multimodal sensing).
 * Experience working with simulators and robots.
 * Experience contributing to research communities or efforts, including publishing papers at conferences (e.g., NeurIPS, CoRL, ICML, ICLR)., * Experience with systematic benchmarking, multimodal data sources, data labeling strategies, and data mixture optimization for frontier or robotics foundation models.
 * Practical experience in "hill-climbing" model performance (e.g., iteratively improving data curation, training recipes, and model benchmarks).
 * Proven ability to build and maintain clean research software and tools for rapid iteration on research ideas.
 * A passion for bringing foundation model research from the lab to robust, real-world robotic systems.

## Description

* Develop data quality, filtering, and labeling methodologies, and optimize multimodal data representations and mixtures to upstream physical and action knowledge into Gemini pre-training.
 * Design and run pre-training and policy training ablations, improve training recipes, and systematically hill-climb physical reasoning and action performance against benchmarks.
 * Build and maintain the research software, evaluation tools, and lightweight post-training workflows required to iterate on research ideas quickly and verify upstream data quality through offline and closed-loop robot policy evaluations.
 * Contribute to our wider research agenda-including vision-language-action (VLA) modeling, world-action models, reinforcement learning, and simulation-and communicate findings clearly in a fast-paced, collaborative team environment.