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Robotics Engineering Interns will build and integrate robotic hardware and software, develop data collection and processing pipelines, implement and evaluate robot learning systems, conduct experiments, troubleshoot hardware and software issues, analyze results, and document implementations. The role spans robot arms, sensors, simulation, machine learning, perception, control, and embodied AI research. Interns will collaborate with researchers and engineers on practical robotics projects and may contribute to algorithm development and technical publications.

Conduct original research in robot learning, embodied AI, and learning from human experience. Develop novel algorithms, representations, and training strategies; design rigorous experiments using multimodal datasets, simulation, and physical robots; analyze results and failure modes; collaborate with scientists and engineers; and contribute to publications and open research artifacts.

Own machine learning models end to end, from data preparation and training through evaluation and production deployment. Build pipelines for large multimodal datasets including video, sensor streams, and language. Collaborate with engineering and computer vision teams, rapidly prototype solutions, apply relevant research, and continuously improve production models and tooling.

Conduct frontier research on robot learning and real-world training data. Design experiments with robotics, vision, and embodied AI models; study how data quality, structure, and diversity affect performance; develop evaluation methods; analyze relevant literature; and partner with engineering and data teams to turn findings into deployed pipeline and product improvements.