Online Calibration & SLAM Engineer
Mecka · Toronto GTA
About Mecka AI
Mecka AI is building the data infrastructure layer for robotics and embodied AI.
We design and operate global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
We work closely with frontier robotics teams to bridge real-world data, simulation, learning-based systems, and deployed hardware.
The Role
We'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.
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.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, we will partner with you to meet your needs.
We may use AI-assisted tools to help evaluate applications against the requirements of this role. These tools support our recruiting team in identifying qualified candidates but do not replace human judgment or limit which applications may be reviewed or considered.