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# Robotics Data Pipeline Engineer

- **Company:** Miraxis AI
- **Location:** Leeds, UK (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Information Engineering, Software Debugging, Python (Programming Language), Data Pipelines
- **Published:** July 3, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2854470639-2

## About the Role

* Hands-on experience with robotics/physical AI datasets (multimodal: video + sensors + proprioception) and their failure modes.
 * Strong Python and data engineering instincts: validation, reproducibility, and careful handling of messy real-world data.
 * Comfort working at the intersection of software and domain: can reason about timing/sync, calibration metadata, and the practicalities of capture pipelines.
 * Able to communicate clearly with both engineers and external stakeholders; converts ambiguity into executable specs.

Nice to have

 * Experience with ROS/ROS2 data formats (bags) or other robotics logging systems.
 * Familiarity with simulation/teleoperation datasets, annotation/labeling workflows, and evaluation harnesses.
 * Experience building QA frameworks that surface issues early (before downstream training).

Working style & expectations

 * Remote-friendly, high-ownership role. Writing and maintaining clear docs is part of the job.
 * Travel may be required occasionally for partner debugging and alignment.

## Description

* Build and operate multimodal pipelines for robotics/physical AI datasets: ingestion, transformation, validation, and delivery packaging.
 * Define training-ready as enforceable checks: alignment validation, integrity checks, schema enforcement, and reproducibility standards.
 * Build tooling to inspect, transform, and audit datasets (large files, long-running jobs, real-world edge cases).
 * Collaborate with Ops/Delivery and Hardware & Integration to ensure capture metadata and formats support downstream usability.
 * Work with partners/vendors/clients to align on formats and best practices; turn external constraints into concrete pipeline requirements.
 * Maintain clear documentation (schemas, runbooks, data contracts) so a remote team can operate consistently.


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