5+ years of experience in SLAM, visual-inertial odometry, and Structure-from-Motion pipelines
Hands-on experience with online or continuous calibration of cameras and IMUs
Strong understanding of multi-view geometry, linear algebra, and optimization
Proficient in C++ and Python for scalable software development
Ability to communicate calibration needs effectively across global teams
Responsibilities
Build continuous camera calibration systems using hardware-synced video and IMU data
Enhance fleet-wide video-based calibration refinement systems
Develop drift monitoring and metrics to flag devices for recall
Maintain and improve the SLAM/VIO stack to report calibration errors
Coordinate with factory calibration and overseas hardware teams on various calibration aspects
Create interactive tools for visualizing calibration metrics and device performance
Benefits
Opportunity to work in a cutting-edge technology environment
Collaborative work with cross-functional teams including computer vision and hardware engineering
Engagement in a production role with tangible impact on device performance
Potential for equity in a growing company
On-site work with periodic travel to hardware sites
Full Job Description
Senior Online Calibration & SLAM Engineer
Location: Toronto GTA or New York (on-site, periodic travel to hardware site) • Type: Full-time • Openings: 1 • Salary: CA$200K - $230K + equity
The role
Own how our client's capture devices stay calibrated after they leave the factory. Cameras and IMUs drift and mounts warp, so a device that shipped in spec can fall out of it. You'll build the systems that catch and correct that drift across the whole fleet, on solid SLAM/VIO foundations. This is a production role, not pure research, working with the computer vision and ML team, factory calibration and hardware engineering.
What you'll do
Build continuous, in-pipeline camera calibration that refines extrinsics from hardware-synced video and IMU data, so fixes ship without device updates
Harden the video-based calibration refinement system fleet-wide, including the monocular wrist-camera path
Build fleet-wide drift monitoring and metrics, flag devices for recall, and own the calibration database and version history
Maintain and improve the SLAM/VIO stack and have it report calibration error as a first-class output
Coordinate with factory calibration and an overseas hardware team on intrinsics, IMU noise and bias, time sync and validation runs
Ship interactive tools (Rerun, Gradio) to visualize trajectories, drift, reprojection error and per-device metrics
What you bring
5+ years building, maintaining or improving SLAM, visual-inertial odometry and Structure-from-Motion pipelines, including production systems
Hands-on online or continuous calibration and rigorous camera + IMU calibration: extrinsics, intrinsics, IMU noise and bias, lens distortion, temporal sync
A fleet and data-quality mindset across many noisy real-world devices
Strong multi-view geometry, linear algebra and optimization fundamentals
Clean, efficient, scalable C++ and Python
Clear communication of calibration requirements to hardware teams across time zones
Nice to have
Markerless or video-based calibration
Kalibr, GTSAM or Ceres Solver; OpenCV, COLMAP, PyTorch, FFmpeg
Fleet observability, calibration databases and versioning
Rerun, Gradio or trajectory and dataset browsers; data pipelines at scale
