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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates 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.
You will 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 and work directly with the CVML team while coordinating 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:
- Develop and maintain continuous, in-pipeline camera calibration that refines extrinsics from recorded, hardware-synced, uncompressed video and IMU
- Fix and harden the video-based calibration refinement system across the fleet, including the monocular wrist-cam path
- Build monitoring and metrics tracking that detects calibration drift across every device, flags devices for recall, and owns the calibration database and version history
- Maintain and improve the SLAM/VIO that online calibration rides on, with calibration-related error as a first-class output
- Coordinate with the factory-calibration engineer and China hardware team on intrinsics limits, IMU noise and bias parameters, temporal synchronization, and validation runs
- 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, including calibration databases and versioning
- Spatial tooling: Rerun, Gradio dashboards, or trajectory and dataset browsers
- Experience with ML infrastructure or data pipelines at scale
