Atlas Research Engineer, SLAM & Spatial AI

Job description

Job responsibilities

• Design and implement real-time SLAM and perception-based state estimation for a mobile humanoid or specialized data collection devices operating in unstructured, dynamic environments
• Build offline 3D reconstruction pipelines (multi-view geometry, SfM/MVS, neural reconstruction, depth/pose fusion) that generate geometric scene structure to inform and supervise large VLM/VLA training
• Pioneer research integrating large VLA and VLM models with 3D spatial perception to enable semantic, language-grounded scene reasoning.
• Bridge classical geometric methods and learned approaches - knowing when to use optimization-based estimation versus learned representations, and how to combine them.
• Write high-quality, maintainable C++ and Python code that fits into a large production codebase.

Job requirements

• PhD in Robotics, Computer Vision, Machine Learning, Computer Science, or related fields (or equivalent experience).
• Prior experience building, and deploying SLAM, visual odometry, or 3D reconstruction systems for robots or autonomous vehicles.
• Strong background in one or more of the following: Real-time SLAM, visual-inertial odometry, and state estimation
• 3D reconstruction (SfM, MVS, multi-view geometry, neural/implicit reconstruction)
• Probabilistic state estimation and sensor fusion (factor graphs, filtering, optimization on manifolds)
• Spatial representations, grounding language/vision into 3D geometry, geometric foundation models
• Solid foundation in the math underlying geometric perception (Lie groups, nonlinear optimization, multi-view geometry).
• Strong analytical and debugging skills; ability to write reliable, well-structured research code in C++ and Python.