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Mecka AI is building the data infrastructure layer for robotics and embodied AI, designing and operating global systems for data capture, labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
You will own how Mecka's devices stay calibrated after they leave the factory. Cameras and IMUs drift over months and mounts warp, so devices 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 the 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 the hardware site.
Key responsibilities:
- Own online calibration: Develop and maintain continuous, in-pipeline camera calibration that refines extrinsics from recorded, hardware-synced, uncompressed video and IMU.
- 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 intrinsics limits, IMU noise and bias parameters, temporal synchronization, and validation runs.
- Build the tooling: Ship interactive tools (Rerun / Gradio) that visualize trajectories, drift over time, reprojection error, and per-device calibration metrics.
Requirements:
Must-have:
- 5+ years of hands-on experience building, maintaining, or improving SLAM, visual-inertial odometry, and Structure-from-Motion pipelines, including systems running in production.
- 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.
- Comfortable owning calibration across many noisy, real-world devices and building the monitoring that catches drift before customers do.
- Intuitive grasp of the linear algebra, optimization, and first principles behind spatial tracking.
- Clean, efficient, scalable C++ and Python.
- Ability to specify calibration requirements clearly to a hardware team across time zones.
Nice-to-have:
- Experience with video-based or markerless calibration and refinement systems.
- Familiarity with sensor-fusion and calibration tools such as Kalibr, GTSAM, or Ceres Solver.
- Experience with 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.
- ML infrastructure or data pipelines that operate at scale.
