Robotics Engineer, Autonomy

About Us

We are working on embodied intelligence. Our mission is to scale general-purpose autonomy for real world problems (the 3Ds), through large-scale learning, multi-modal data, and robust control.

We are looking for passionate engineers and scientists who thrive at the intersection of machine learning, robotics, and systems engineering, and want to see their research come alive in real robots.

Role Overview

You will lead development of the algorithms and architectures that enable our robots to interact with and reason about the physical world. This role bridges world model research, embodied AI, and real-time robotics. You will engineer models and learning systems that power robotic agents through real world jobs.

Responsibilities

Train and adapt large-scale VLA & VLMs that predict multi-modal futures (video, proprioception, audio, actions)

Develop systems for cross-modal grounding, enabling robots to interpret sensor data in context and build coherent world models

Enable temporal reasoning and goal-directed behavior through hierarchical task decomposition and meta-reasoning

Support human-robot interaction by recognizing intentions, interpreting social cues, and enabling collaborative workflows

Deploy models into real-time humanoid and mobile robots

Evaluate and scale pipelines to measure generalization and safety

Collaborate with locomotion, simulation and hardware teams to bridge sim-to-real transfer

Publish and open source datasets, models, papers in parallel

Preferred Qualifications

BS/MS/PhD in Robotics, AI/Computer Science, or related field

Proficiency in Python and C++, and deep learning frameworks (PyTorch / JAX)

Deep experience in GenAI, RL/IL, control, or multimodal learning

Understanding of scaling laws, evaluation metrics, and scaling training for large models

Familiarity with real-robot systems, sensing, and embedded control integration

Familiarity with industry SOTA and latest research, e.g. Gr00t, Pi0, etc

Bonus Skills

Experience with transformer-based control policies or diffusion policy learning.

Work on humanoid locomotion, manipulation, or whole-body coordination.

Prior open-source or research contributions in robotics, control, or deep learning.