Research Scientist

Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.

The Role

As a Research Scientist on the AI Research team, you will own the end-to-end loop of Embodied AI. You will leverage our massive, "in-the-wild" multimodal datasets to develop models that don’t just move - they generalize, self-improve, and redefine what is possible in unstructured human environments.

What You’ll Do

• Advance the Frontier: Design and implement state-of-the-art robot learning algorithms (RL, Imitation Learning, Diffusion) for complex, high-DOF manipulation tasks.

• VLA & Video Generation: Architect and scale multi-modal foundation models (VLMs/VLAs) and leverage generative video architectures to enhance world-modeling and robot foresight.

• End-to-End Ownership: Own the full research-to-production pipeline: from task definition and data curation to large-scale training and real-time deployment on physical hardware.

• Physical-World Evaluation: Move beyond the benchmark. You will evaluate and iterate on models using our expanding robot fleet, ensuring commercial-grade performance in dynamic customer environments.

• Collaborative Leadership: Work at the intersection of AI, Hardware, and Teleop teams to enhance the "Intelligence-per-Watt" of our robotic systems.

What You’ll Bring

• Research Excellence: A PhD (preferred) or Master’s in CS/Robotics with a significant publication record in top-tier conferences (RSS, CoRL, ICRA, NeurIPS, CVPR, or ICLR).

• 5+ Years of Mastery: Deep experience in AI and robotics research, specifically in deploying deep learning models onto physical robots (not just simulation).

• Generative AI Expertise: Hands-on experience with Vision-Language-Action (VLA) models, Video Generation, or large-scale transformer architectures.

• Technical Stack: Mastery of Python and PyTorch (or JAX). Familiarity with simulation stacks like MuJoCo, Isaac, or Drake.

• High-Quality Code: The ability to write maintainable, production-ready research code that scales across GPU clusters.

Bonus Points For

• Experience with founding-stage startups or leading small, elite research teams.

• Deep understanding of classical robotics (controls, state estimation) to complement learning-based approaches.

• Experience profiling and optimizing foundation models for low-latency inference on edge devices.

Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don't check every box.