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

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.

Beneficial Skills

• Dataset Curation: Experience building curation steps for machine learning datasets, such as filtering, deduplication, and split design.
• Synthetic Data: Familiarity with combining simulated and real data in one training dataset.
• C++ Development: Experience writing C++ for performance-critical capture or driver code.
• Robot Learning Datasets: Familiarity with dataset conventions used in robot learning, such as LeRobot.
• Teleoperation Data: Experience recording teleoperation data for imitation learning.