Robotics Data Engineer (m/f/d) Data Generation & Curation

About this role

The AI Teams at Agile Robots are looking for a Robotics Data Engineer (m/f/d) Data Generation & Curation , who will build the pipeline that records, curates, and prepares multimodal robot datasets for foundation-model training. The pipeline has to run across different robot setups and combine real recordings with synthetic data from simulation. Responsibilities Multimodal Capture: Build ROS 2 based capture for RGB and RGB-D cameras, wrist cameras, joint encoders, force-torque sensors, and teleoperation signals.

Synchronization: Keep multimodal streams time-aligned through sensor synchronization, calibration, and timestamp integrity checks. Dataset Health: Define and run health checks that detect dropped frames, sync drift, corrupt frames, and missing modalities. Dataset Curation: Filter and deduplicate recorded data, maintain dataset metadata, and define train and evaluation splits. Synthetic Data Integration: Integrate synthetic data generated in simulation into the same datasets and formats as real recordings.

Preprocessing: Build preprocessing that aligns, slices, and inspects recorded episodes into a form that foundation-model training can use directly. Formats & Release: Design dataset schemas and storage formats, version datasets so experiments can be reproduced, and package them for external release where approved. Essential Skills Engineering Background: Degree in Computer Science, Robotics, Electrical Engineering, or a related field, or equivalent qualification plus professional experience building comparable data or robotics software systems.

ROS 2 Pipelines: Hands-on experience building sensor recording or data pipelines in ROS 2 with Python. Sensor Synchronization: Experience synchronizing multiple cameras and sensors, including calibration and handling timestamp drift. Dataset Schemas: Experience designing schemas and storage layouts for multimodal time-series data, in formats such as Parquet, Zarr, or HDF5. Camera Pipelines: Working knowledge of camera pipelines and video encoding, and how they affect data size and frame integrity.