• Research and experiment with new ML models for perception and/or complex manipulation tasks including:
• Robot Foundation Model : VLA, VLM, etc
• Spatial AI : 3D reconstruction, neural rendering, SLAM, etc.
• State Estimation : pose estimation, tracking, depth estimation, etc.
• Robot Learning : imitation learning, reinforcement learning, etc.
• Multimodal Fusion: model combining different sensor inputs and modalities.
• Collaborate with robotics, hardware, and product teams to ensure seamless AI model integration.
• Conduct experiments and evaluations with the goal of advancing state-of-the-art performance and contributing to research publications.

Requirements

• Currently pursuing or recently completed a PhD degree in Computer Science, Robotics, Machine Learning, or a related field.
• Proficiency in multimodal models, modern ML architectures (e.g., transformers, diffusion models), and reinforcement learning.
• Proficiency in deep learning frameworks such as PyTorch, JAX, or TensorFlow.
• Requires 5 days/week in-office collaboration with the teams.

Bonus Qualifications

• Exposure to robot learning through tactile and/or vision-based sensors.
• Expertise in object pose estimation, bimanual manipulation, and synthetic data.

Benefits

• Health, dental, and vision insurance
• Lunches and dinners