World Labs

World Labs

Research Engineer / Scientist (SLAM)

San Francisco

Sponsorship not specified$250k-$350kDetected 90 days ago
PythonC++Machine LearningComputer VisionA/B TestingSystems EngineeringRoboticsSensorsResearchMentoring

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.
  • We're looking for a SLAM Specialist to design, implement, and advance state-of-the-art simultaneous localization and mapping systems that enable accurate, robust spatial understanding from real-world sensor data.
  • You'll collaborate closely with research scientists, ML engineers, and systems teams to translate cutting-edge SLAM ideas into production-ready capabilities that form the backbone of our world modeling stack.

Requirements

  • 6+ years of experience working on SLAM, state estimation, robotics perception, or related areas.
  • 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.
  • Strong understanding of real-world sensor characteristics, calibration, synchronization, and noise modeling.
  • 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).
  • Familiarity with learning-based perception or representation learning and how it can augment classical SLAM pipelines.
  • Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.

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

  • 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
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This listing is sourced directly from World Labs's careers page and normalized into a canonical job model.