World Labs
Research Engineer / Scientist (SLAM)
San Francisco · Senior
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About the role
- This role is focused on modern SLAM techniques-both classical and learning-based-with an emphasis on scalable state estimation, sensor fusion, and long-term mapping in complex, dynamic environments.
- This is a hands-on, research-driven role for someone who enjoys working at the intersection of robotics, computer vision, and probabilistic inference.
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.
- 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.
Requirements
- 6+ years of experience working on SLAM, state estimation, robotics perception, or related areas.
Compensation
- $250,000-$350,000 base salary (good-faith estimate for San Francisco Bay Area upon hire
- Total Compensation
- Base salary plus equity awards
- Salary History
- We do not request or consider prior compensation in making offers
- Cal. Lab. Code §1197.5 (Equal Pay Act)
Benefits
- Strong foundation in probabilistic estimation, optimization, and geometric vision (e.g., bundle adjustment, factor graphs, Kalman filtering).
- Research and prototype learning-based or hybrid SLAM approaches that combine classical geometry with modern machine learning methods.
Equal opportunity
- World Labs is an equal opportunity employer.
- We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law.
- We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
- California Pay Transparency
This listing is sourced directly from World Labs's careers page and normalized into a canonical job model.