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The Surreal Spatial AI group is seeking high-performing Research Scientists to build machine perception technology allowing AI agents, and systems to perceive, understand, and reason about the 3D world around them. The aim of this role is to develop advanced algorithms for active perception and intelligent interaction. You will investigate novel architectures combining World Models, data-driven control, and Machine Perception for real-time applications. Leveraging data from egocentric devices (Project Aria) and robotic platforms, your work will span the full stack—from high-fidelity 3D understanding to the predictive modeling of dynamics and actions—empowering agents to reason about and manipulate their surroundings.
Job Responsibility:
Lead, collaborate, and execute on research that pushes forward the state of the art in 3D computer vision, embodied reasoning, and/or predictive world modeling
Directly contribute to experiments, including designing experimental details, authoring reusable code, running evaluations, and organizing results
Work with the team to design practical experiments and prototype systems related to dynamic scene modeling, long-horizon reasoning, and machine perception
Contribute to publications and open-sourcing efforts
Help identify long-term ambitious research goals as well as intermediate milestones
Requirements:
Currently has or is in the process of obtaining a PhD in the field of Computer Vision, Robotics, AI, Computer Science, a related field, or equivalent practical experience
Experience communicating research for public audiences of peers
Experience with real-world system building and data collection, including design, coding, and evaluation with modern ML methods
Research experience involving 3D Computer Vision, Deep Learning, or Reinforcement Learning—specifically related to scene understanding, generative modeling, autonomous agents, or robotic control
Experience in developing and debugging in C/C++, Python, or Rust
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Nice to have:
Hands-on experience implementing 3D computer vision algorithms and training/evaluating large-scale ML/AI models
Familiarity with Reinforcement Learning (RL), VLAs, control theory, or learning-based planning
Experience bridging the gap between perception and action (e.g., Active Vision, Embodied AI, Inverse RL, or RLHF)
Experience with physics simulators or synthetic environments (e.g., Habitat, MuJoCo, Isaac Lab)
Experience working in a Unix environment
Demonstrated research and software engineering experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g., GitHub)
Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops, journals, or conferences such as CVPR, CoRL, ICRA, RSS, NeurIPS, ECCV, ICCV, IROS, or similar