5+ years of experience in machine learning and robotics research.
Master's degree or relevant experience in Computer Science, Robotics, or a related field.
Solid understanding of deep learning fundamentals, including Transformer architectures.
Experience debugging and optimizing deep learning models in production environments.
Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
Familiarity with robot simulation platforms and model deployment on physical robots.
Ability to convert ambiguous real-world challenges into actionable research problems.
Responsibilities
Research and develop large-scale deep learning models for robotic manipulation tasks.
Design and test new robot learning algorithms like reinforcement learning on both simulations and physical robots.
Analyze model training outcomes and debug to enhance overall system performance.
Conduct experiments to identify issues and guide iterative model improvements.
Collaborate with teams to improve data collection and filtering methods.
Create tools for tracking model performance metrics and visualizing data anomalies.
Translate product needs into specific research or engineering tasks.
Benefits
Hybrid work environment combining research and engineering tasks.
Opportunity to work with cutting-edge AI and robotics technology.
Collaboration with experienced professionals from top tech backgrounds.
Contribution to products that redefine robotic automation.
Engagement in high-impact projects with real-world applications.
Full Job Description
Position Overview
As a Research Engineer 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 the intersection of geometry and robotics, we want to hear from you-even if you don't check every box.
