Research Engineer / Scientist

Responsibilities

• Design and implement modern SLAM systems for real-world environments, including visual, visual-inertial, lidar, and multi-sensor configurations.
• Develop localization and mapping pipelines covering pose estimation, map management, loop closure, and global optimization.
• Research and prototype learning-based or hybrid SLAM approaches combining classical geometry with machine learning.
• Build and maintain scalable state-estimation frameworks using factor-graph optimization, filtering, and smoothing.
• Develop sensor-fusion strategies integrating cameras, IMUs, depth sensors, lidar, and other modalities.
• Analyze real-world failure modes such as perceptual aliasing, dynamic scenes, and drift, and develop principled solutions.
• Create evaluation frameworks, benchmarks, and metrics for SLAM accuracy, robustness, and performance across large datasets.
• Optimize real-time performance, memory usage, and compute efficiency for large-scale production systems.
• Collaborate with reconstruction, simulation, and infrastructure teams to integrate SLAM outputs into downstream world-modeling and rendering pipelines.
• Propose research ideas, mentor teammates, and help define localization and mapping best practices.

Requirements

• 6+ years of experience in SLAM, state estimation, robotics perception, or related areas.
• Strong foundation in probabilistic estimation, optimization, and geometric vision, including bundle adjustment, factor graphs, and Kalman filtering.
• Deep experience with visual, visual-inertial, lidar, multi-sensor, or hybrid SLAM systems.
• Proficiency in Python and/or C++, with 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 applied to classical SLAM pipelines.
• Strong understanding of sensor characteristics, calibration, synchronization, and noise modeling.
• Ability to drive projects from concept through deployment in ambiguous, fast-moving environments.
• Strong engineering rigor, ownership, and focus on correctness, stability, and measurable improvements.
• Collaborative approach and commitment to high-quality design, experimentation, and code.