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 r
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Your mission dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing.
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Warum dieser Job gelistet ist
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- A dynamic high-tech company, combined with financial soundness and world-class investors. - Join an interdisciplinary, international team with 60+ different nationalities in a collaborative work environment. - Lots of development opportunities as we continue to grow. - Challenging tasks and impactful projects alongside experts that enable professional and personal growth. - Corporate
Agile Robots is a leading provider of AI-powered robotics solutions. By combining artificial intelligence and robotics, the company makes industries smarter, more flexible, and more efficient. Founded in Munich in 2018 by renowned robotics researchers from the German Aerospace Center (DLR), Agile Robots has experienced rapid global growth.
Today, more than 3,500 highly skilled robotics and AI enthusiasts are employed worldwide. The company has one of the largest research and development teams in the AI and robotics industry. Agile Robots has built a unique portfolio; together with the subsidiaries BÄR Automation, Franka Robotics, idealworks, and Krause Automation, the company covers all areas of AI-driven robotics.
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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.
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Zur OriginalanzeigeModelle bauen, trainieren und verbessern: von klassischem Machine Learning über Computer Vision bis zum Feinschliff großer Sprachmodelle.