ML Data Engineer; m​/f​/d; Sensor Data & Pipelines

• 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

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  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.

• 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

• 5+ years of hands-on experience in Python and data processing frameworks (Pandas, Num Py, 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, MM Detection, 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