ML Data Engineer (m/f/d) - Sensor Data & Pipelines

What we offer Work in an international, agile team creating the future of autonomous systems Grow your career in a expanding and ambitious engineering team Build innovative products using state-of-the-art technologies in AI, robotics, and autonomy Benefit from a steep learning curve and continuous development Enjoy team events and a strong, collaborative culture Your mission This role owns the data foundation of our perception systems end-to-end — the layer that directly determines model performance in real-world environments. You'll set the technical direction for how we collect, curate, and continuously improve the datasets behind object detection, working as a senior technical partner to ML, perception, and robotics teams — turning raw, messy sensor data into reliable, production-grade systems at scale. You will take full ownership of the ML data lifecycle — from architecture decisions on ingestion and pipelines, through labeling strategy and QA, to driving continuous, metrics-informed dataset improvement — and will be expected to bring judgment and prior experience to how this is done, not just execute a defined process. What you'll do: Architect and own scalable pipelines for ingesting, organizing, and preprocessing large volumes of time-series camera and multi-sensor data (RGB, IR, thermal, depth, IMU) Drive the strategy behind our object detection datasets, ensuring quality, diversity, and statistical representativeness at scale Design and operate active learning loops that connect model performance directly to data selection and improvement priorities Own labeling workflows end-to-end — tooling decisions, QA methodology, consistency standards, and coordination of annotation efforts Partner closely with AI Engineers to diagnose model weaknesses, bias, and drift, and translate findings into concrete dataset strategy Plan and lead data collection campaigns (field recordings, drone/video capture) to close gaps with high-value real-world data Build internal tools and dashboards that give the org visibility into dataset quality, distribution, and performance gaps Your profile 5+ years of hands-on experience in Python and data processing frameworks (Pandas, NumPy, vectorized operations, multiprocessing) Proven track record building and owning ETL/ELT pipelines for large-scale video and sensor datasets in production Deep experience with data orchestration and lifecycle management for ML/computer vision workflows, including dataset versioning and reproducibility Strong command of object detection pipelines (Detectron2, MMDetection, COCO format, bounding-box standards) Demonstrated experience designing active learning, uncertainty sampling, or semi-supervised dataset workflows Deep familiarity with data annotation platforms (CVAT, Label Studio) and building automated QA/consistency checks Strong grasp of evaluation metrics for object detection (IoU, mAP, precision-recall curves, class-wise metrics) Comfortable owning decisions around databases (SQL