# Research Engineer / Scientist

**Company:** [World Labs](https://hotfix.jobs/companies/world-labs)
**Location:** San Francisco, CA
**Role:** AI Research
**Salary:** $250k – $350k/yr
**Experience:** 6+ years
**Skills:** Slam, State Estimation, Robotics Perception, Python, C++, Probabilistic Estimation, Geometric Vision, Bundle Adjustment, Factor Graphs, Kalman Filtering, Sensor Fusion, Lidar, Computer Vision, Numerical Optimization, Machine Learning
**Posted:** 2026-02-17

> Research-driven engineer or scientist developing production-ready SLAM, localization, mapping, and sensor-fusion systems for spatial intelligence. Requires 6+ years of experience, strong probabilistic estimation and geometric vision expertise, and proficiency in Python and/or C++.

## Job Description

## 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.


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