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

autonomous-teaming·Munich (DEU)senior

En corto

→Construyes y gestionas pipelines a gran escala para datos de sensores (cámara, IR, térmico, etc.) en sistemas autónomos.
→Diriges estrategias de datos para detección de objetos: calidad, diversidad, etiquetado, y bucles de aprendizaje activo.
→Eres el puente técnico clave entre IA, percepción y robótica, con responsabilidad total sobre el ciclo de vida de los datos.

Fluent in English

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¿Qué piden?

✓5+ años en Python y procesamiento de datos (Pandas, NumPy, multiprocessing)
✓Experiencia comprobada con pipelines ETL/ELT para datos de video y sensores a gran escala
✓Dominio de gestión de ciclos de vida ML: versionado, reproducibilidad, dataset quality
✓Experiencia con pipelines de detección de objetos (Detectron2, MMDetection, COCO)
✓Conocimiento en bucles de aprendizaje activo, sampling por incertidumbre y workflows semi-supervisados
✓Habilidad para liderar proyectos de recolección de datos y gestionar workflows de etiquetado

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonPandasNumPyvectorized operationsmultiprocessingETL/ELTdata orchestrationdataset versioningreproducibilityDetectron2

¿A quién escribirle en autonomous-teaming?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

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/NoSQL), file systems, and large-scale image, video, and sensor dataset management Track record of working cross-functionally and influencing perception, deployment, robotics, and data infrastructure teams Fluent in English; German and/or French are a plus Nice to have Experience with cloud storage and MLOps tools (AWS S3, MinIO, ClearML, MLFlow, Weights & Biases). Familiarity with ROS / robotics data formats (bag files, TF trees, sensor_msgs), Docker, or embedded ML workflows. Prior work with robotics, drones, or multi-sensor perception systems, including IR, LiDAR, radar, or audio datasets. What else Outside-the-box creativity with a blend of conceptual and systematic design thinking. High intrinsic motivation, attention to detail, and strong problem-solving mindset. Structured, methodical, and reliable execution, even under uncertainty. Humble, collaborative, and mission-driven — values collective success over ego. High ethical standards and disciplined work ethic.

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