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# Platform Engineer - Robot Data Infrastructure (human)

- **Company:** Neura Robotics GmbH
- **Location:** Riederich, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Cloud Database, Codecs, Continuous Integration, Data Infrastructure, DevOps, Middleware, FFmpeg, H.264/MPEG-4 AVC, Python (Programming Language), Raw Data, Ansible, TypeScript, Multi-Agent Systems, Backend, Containerization, Infrastructure Automation Frameworks, Multiaccess Edge Computing, Terraform, Data Pipelines, Docker
- **Published:** October 9, 2026
- **Apply:** https://startup.jobs/platform-engineer-robot-data-infrastructure-human-neura-robotics-8333955

## About the Role

* Backend engineering: Strong Python and TypeScript skills in production backend systems.
 * Cloud: Hands-on cloud experience, AWS preferred.
 * Robotics middleware: Experience with ROS 2/DDS or similar robotics middleware.
 * Media codecs: Familiarity with video/audio codecs and compression (FFmpeg, PyAV, H.264/AV1).
 * Infrastructure-as-code: Ansible, Docker, and Terraform for fleet management.
 * DevOps mindset: You care about CI/CD, automation, and keeping things shippable.
 * ML data pipelines: Interest in or exposure to ML data pipelines and robot learning.

## Description

You design, build, and operate the end-to-end data infrastructure that turns raw multi-modal robot sensor recordings into ML-ready datasets. Working at the intersection of robotics, edge computing, and cloud data engineering, you own the full pipeline, from physical data collection environments to training-ready formats for robot learning.

 * Data format & pipeline design: Define and evolve internal data formats for multi-modal sensor streams (video, depth, audio, point clouds, joint states, tactile) with efficient compression.
 * Edge-to-cloud ingestion & transformation: Build services that orchestrate on-premise recording, validate data quality, transfer large volumes to the cloud, and convert raw data into structured formats for ML consumption.
 * Infrastructure automation: Provision and manage on-premise compute via infrastructure-as-code, and handle containerized deployments and rolling updates across distributed sites.
 * Platform services & tooling: Develop backend services (TypeScript, Python), shared libraries, CLI tools, and developer documentation.
 * Multi-agent annotation: Orchestrate vision-language agents that auto-annotate multi-modal episodes at scale, with human-in-the-loop review to keep labels training-ready.
 * Cross-functional collaboration: Work closely with hardware, robotics, and AI/data science teams to onboard new sensor modalities and track data throughput KPIs.