Weave was founded to build the robots we’d want to have in our own home. We believe the next generation of robotics will transform everyday life by enabling people to do more and to reclaim time to spend on what’s important.
We also believe robots are in a sense like any other product: to matter, they have to ship. Our robots are already operating in real homes and businesses, giving us the opportunity to rapidly improve from real-world experience. With a growing team, strong customer demand, and capital for expansion, we’re entering an exciting stage of growth—and we’re looking for people with exceptional talent and standards to help bring home robotics to millions of households.
As Weave’s robots operate in real customer homes, they need to constantly understand where they are and how the world around them is changing . Unlike robots operating in mapped warehouses or other controlled environments, they must localize reliably through changing lighting, moving people and objects, visually sparse spaces, and environments that evolve from day to day.
As a 3D Perception & SLAM Engineer at Weave, you’ll build the localization, mapping, and navigation systems that make this possible. You’ll work across state estimation, sensor fusion, calibration, and perception infrastructure to turn cameras, IMUs, odometry, and other onboard sensing into an accurate, real-time understanding of the robot and its environment. This role straddles application and research: you’ll be pushing the boundaries of established techniques and investigating how to apply new, end-to-end learned methods in our stack.
Build production-grade vSLAM: Build and optimize Weave’s indoor vSLAM and localization system by fusing cameras, IMUs, wheel odometry, and other onboard sensors to deliver robust, real-time robot localization.
Develop sensor infrastructure: Build software infrastructure for sensor calibration, synchronization, and data fusion to ensure accurate, low-latency perception across the robot platform.
Own the localization stack: Architect localization and mapping from core estimation algorithms through sensor integration, enabling reliable operation in dynamic indoor environments.
Advance the state of the art: Explore and evaluate new approaches to localization and mapping, combining proven geometric methods with learned techniques and bringing promising research into production on real robots.
Optimize for the real world: Improve the robustness, computational efficiency, and power consumption of perception systems designed to operate continuously in customers’ homes.
Debug on real robots: Diagnose localization failures using logs, recorded sensor data, simulation, and deployed hardware, turning difficult real-world edge cases into systematic improvements.
Strong vSLAM expertise: 3+ years’ experience building vision-based SLAM and sensor calibration for real-world robotic applications.
State estimation fundamentals: Deep understanding of state estimation, sensor fusion, probabilistic estimation, and techniques such as Kalman filtering, factor graphs, bundle adjustment, or nonlinear optimization.
Multi-sensor systems experience: Strong experience with multi-sensor calibration, time synchronization, and integrating cameras, IMUs, odometry and/or LiDAR into robust perception pipelines operating within strict realtime constraints.
Excellent software engineering skills: Experience writing production-quality software in C++ or Python. Experience with ROS, embedded systems, or high-performance inference frameworks is a plus.
Experience building localization systems robust to dynamic scenes, changing environments, sensor degradation, or long-duration operation.
Experience building SLAM systems for mobile manipulators.
Experience evaluating or integrating learned components for visual odometry, feature extraction and matching, depth, or place recognition.
Experience with learned or end-to-end approaches to visual odometry, localization, mapping, depth estimation, feature extraction/matching, or sensor fusion.
Experience with state of the art 3D reconstruction.
Familiarity with visual geometry transformers, gaussian splatting, VLAs.
Contributions to OSS SLAM systems or published work in the area.
Skills Required
- 3+ years building vision-based SLAM and sensor calibration for real-world robotic applications
- Deep understanding of state estimation, sensor fusion, probabilistic estimation (Kalman filtering, factor graphs, bundle adjustment, nonlinear optimization)
- Strong experience with multi-sensor calibration, time synchronization, and integrating cameras, IMUs, odometry and/or LiDAR
- Experience writing production-quality software in C++ or Python
- Experience with ROS, embedded systems, or high-performance inference frameworks
- Experience building localization systems robust to dynamic scenes, changing environments, sensor degradation, or long-duration operation
- Experience building SLAM systems for mobile manipulators
- Experience evaluating or integrating learned components for visual odometry, feature extraction/matching, depth, or place recognition
- Experience with state-of-the-art 3D reconstruction
- Familiarity with visual geometry transformers, gaussian splatting, VLAs
- Contributions to open-source SLAM systems or published research in the area
What We Do
Weave Robotics is a San Francisco-based startup focused on developing practical, autonomous personal robots for the home. Their debut product, Isaac 0, is a stationary laundry-folding robot designed to save users time by autonomously tidying and folding clothes. The company aims to transition advanced robotics research into real-world home products, starting with laundry as a primary use case to return time to households.
