Research Engineer / Scientist (SLAM)

Responsibilities

• Design and implement modern SLAM systems for real-world environments, including visual, visual-inertial, lidar, or multi-sensor configurations.
• Develop robust localization and mapping pipelines, including pose estimation, map management, loop closure, and global optimization.
• Research and prototype learning-based or hybrid SLAM approaches that combine classical geometry with modern machine learning methods.
• Build and maintain scalable state estimation frameworks, including factor graph optimization, filtering, and smoothing techniques.
• Develop sensor fusion strategies that integrate cameras, IMUs, depth sensors, lidar, or other modalities to improve robustness and accuracy.
• Analyze failure modes in real-world SLAM deployments (e.g., perceptual aliasing, dynamic scenes, drift) and design principled solutions.
• Create evaluation frameworks, benchmarks, and metrics to measure SLAM accuracy, robustness, and performance across large datasets.
• Optimize performance across the stack, including real-time constraints, memory usage, and compute efficiency, for large-scale and production systems.
• Collaborate with reconstruction, simulation, and infrastructure teams to ensure SLAM outputs integrate cleanly with downstream world modeling and rendering pipelines.
• Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping define best practices for localization and mapping across the organization.

Requirements

• 6+ years of experience working on SLAM, state estimation, robotics perception, or related areas.
• Strong foundation in probabilistic estimation, optimization, and geometric vision (e.g., bundle adjustment, factor graphs, Kalman filtering).
• Deep experience with one or more SLAM paradigms (visual, visual-inertial, lidar, multi-sensor, or hybrid systems).
• Proficiency in Python and/or C++, with hands-on experience building research or production-grade SLAM systems.
• Experience with numerical optimization libraries and/or robotics frameworks.
• Familiarity with learning-based perception or representation learning and how it can augment classical SLAM pipelines.
• Strong understanding of real-world sensor characteristics, calibration, synchronization, and noise modeling.
• Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.
• A strong sense of ownership and engineering rigor: you care deeply about correctness, stability, and measurable improvements.
• Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality.