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 sits at the core of our perception systems, owning the data that directly drives model performance in real-world environments. You will work closely with ML, perception, and robotics teams, focusing on building, curating, and continuously improving datasets behind object detection — turning raw sensor data into reliable, high-performing systems.

You will take end-to-end ownership of the ML data lifecycle, from data collection and ingestion to labeling, quality assurance, and continuous dataset improvement, ensuring data is representative, high-quality, and production-ready.

What you'll do:

Design and maintain scalable pipelines to ingest, organize, and preprocess large volumes of time-series camera and multi-sensor data (RGB, IR, thermal, depth, IMU)
Own and continuously improve object detection datasets, ensuring quality, diversity, and statistical representativeness
Build and operate active learning loops, connecting model performance with data selection and improvement
Manage labeling workflows end-to-end, including tooling, QA validation, consistency checks, and coordination of annotation efforts
Collaborate with ML Engineers to evaluate models and translate weaknesses, bias, and drift into actionable dataset improvements
Plan and execute data collection campaigns (e.g. field recordings, drone/video capture) to acquire high-value real-world data
Create internal tools and dashboards to analyze dataset quality, distributions, and performance gaps

Strong experience in Python and data processing frameworks (Pandas, NumPy, vectorized operations, multiprocessing).
Hands-on experience building ETL/ELT pipelines for ingesting, transforming, and structuring large video and sensor datasets.
Experience with data orchestration and lifecycle management for ML and computer vision workflows, including dataset versioning and reproducibility.
Solid understanding of object detection pipelines (Detectron2, MMDetection, COCO format, bounding-box standards).
Experience with active learning, uncertainty sampling, or semi-supervised dataset workflows.
Familiarity with data annotation platforms (CVAT, Label Studio) and automated QA/consistency checks.
Strong grasp of evaluation metrics for object detection (IoU, mAP, precision-recall curves, class-wise metrics).
Comfortable with databases (SQL/NoSQL), file systems, and the management of large-scale image, video, and sensor datasets.
Ability to work cross-functionally with perception, deployment, robotics, and data infrastructure teams.
Fluent in English, German and/or French are a plus

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.

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.
Extra-curricular achievements, leadership, or unique projects are a plus.
NATO-aligned nationality or close ally citizenship is required.

Join us to shape the future of AI-driven defense!

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