The RoleWe're looking for a hands-on Senior SLAM & Calibration Engineer to own how Mecka's devices stay calibrated after they leave the factory. Cameras and IMUs drift over months 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: online (in-pipeline) camera calibration, video-based calibration refinement, fleet-wide drift monitoring, and the tools operations and researchers use to track it, all built on solid SLAM/VIO foundations.
This is a production role, not pure research: you'll own systems that run across the real fleet. You'll work directly with the CVML team and coordinate with our factory-calibration engineer and hardware team. Because the work runs on real, hardware-synced recordings and is validated on physical devices, this is an on-site role with periodic travel to our hardware site.
What you'll do- Own online calibration: Develop and maintain continuous, in-pipeline camera calibration that refines extrinsics from recorded, hardware-synced, uncompressed video and IMU - the pipeline equivalent of on-device calibration, so fixes ship without a device software update.
- Rebuild video-based calibration: Fix and harden the video-based calibration refinement system across the fleet, including the monocular wrist-cam path, and drive it against real benchmarks.
- Monitor the fleet: Build the monitoring and metrics tracking that detects calibration drift across every device, flags devices for recall, and owns the calibration database and version history.
- Make SLAM carry calibration: Maintain and improve the SLAM/VIO that online calibration rides on, and have it emit calibration-related error as a first-class output.
- Bridge to hardware: Coordinate with the factory-calibration engineer and the China hardware team on the intrinsics limit (online refinement fixes extrinsics; intrinsics need a marker at the factory), IMU noise and bias parameters, temporal synchronization, and validation runs that avoid local minima.
- Build the tooling: Ship interactive tools (Rerun / Gradio) that visualize trajectories, drift over time, reprojection error, and per-device calibration metrics for operations and researchers.
What You Bring Must-have:
- SLAM / VIO / SfM depth: 5+ years of hands-on experience building, maintaining, or improving SLAM, visual-inertial odometry, and Structure-from-Motion pipelines, including systems running in production.
- Continuous and multi-sensor calibration: Hands-on experience with online or continuous calibration and rigorous camera + IMU calibration: extrinsics, intrinsics and their limits, IMU noise density and random-walk bias, lens-distortion models, and temporal synchronization.
- Fleet and data-quality mindset: You're comfortable owning calibration across many noisy, real-world devices, and you build the monitoring that catches drift before customers do.
- Multi-view geometry and optimization: An intuitive grasp of the linear algebra, optimization, and first principles behind spatial tracking.
- Engineering rigor: Clean, efficient, scalable C++ and Python.
- Cross-border collaboration: You can specify calibration requirements clearly to a hardware team across time zones.
Nice-to-have:
- Markerless calibration: Experience with video-based or markerless calibration and refinement systems.
- Calibration frameworks: Sensor-fusion and calibration tools such as Kalibr, GTSAM, or Ceres Solver.
- 3D vision and ML libraries: OpenCV, COLMAP, PyTorch, and FFmpeg.
- Fleet observability: Monitoring at scale, including calibration databases and versioning.
- Spatial tooling: Rerun, Gradio dashboards, or trajectory and dataset browsers.
- Scale: ML infrastructure or data pipelines that operate at scale.
A Note on ApplyingStudies show women and candidates from underrepresented groups often only apply when they meet 100% of the listed qualifications, while others apply after meeting 60%. If you don't check every box above but believe you can do the job, we encourage you to apply - we're looking for capability and trajectory, not a perfect checklist match.
