Research Scientist (VLAs)

For researchers, this means an opportunity to:

• Work on fundamental challenges in robotics and AI: multimodal learning, tactile-rich manipulation, sim-to-real transfer, and large-scale benchmarking.

• Access state-of-the-art infrastructure: hundreds of humanoid robots, GPU clusters, high-fidelity simulators, and a global-scale evaluation pipeline.

• Collaborate with leading experts across academia and industry, and publish results that will shape the next decade of robotics.

• Contribute to an initiative that will redefine the future of embodied AI—with all results made open to the world.

As we prepare for our official launch on October 1, 2025, we are assembling a world-class team ready to pioneer the next era of robotics.

We invite ambitious researchers and engineers to join us in this bold challenge to rewrite the history of robotics.

Responsibilities

• Design and implement data preprocessing pipelines for multimodal robot datasets

• Train VLA models using supervised learning, RL, fine-tuning, RLHF, and training from scratch

• Develop and evaluate models in both simulation and on physical robots

• Improve training robustness and efficiency through algorithmic innovation

• Analyze model performance and propose enhancements based on empirical results

• Deploy VLA models onto real humanoid and mobile robotic platforms

• Publish research in top-tier conferences (e.g., NeurIPS, CoRL, CVPR)

Requirements

• MS degree with 3+ years of industry experience, or PhD in Computer Science, Electrical Engineering, or a related field.

• Have at least one first-author publication in a top-tier conference such as CoRL, ICML, CVPR, NeurIPS, IROS, ICLR, ICCV, or ECCV.

• Experience with open-ended learning, reinforcement learning, and frontier methods for training LLMs/VLMs/VLAs such as RLHF and reward function design

• Experience working with simulators or real-world robots

• Knowledge of the latest advancements in large-scale machine learning research

• Experience with deep learning frameworks such as PyTorch

Nice to haves

While not specifically required, tell us if you have any of the following.

• PhD or equivalent research experience in robot learning.

• Practical experience implementing advanced control strategies on hardware, including impedance control, adaptive control, force control, or MPC.

• Experience using tactile sensing for dexterous manipulation and contact-rich tasks.

• Familiarity with simulation platforms and benchmarks (e.g., MuJoCo, PyBullet, Isaac Sim) for training and evaluation.

• Proven track record of achieving significant results as demonstrated by publications at leading conferences in Machine Learning (NeurIPS, ICML, ICLR), Robotics (ICRA, IROS, RSS, CoRL), and Computer Vision (CVPR, ICCV, ECCV)

• Strong end-to-end system building and rapid prototyping skills

• Experience with robotics frameworks like ROS