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 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
Your profile
- 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
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.
- Extra-curricular achievements, leadership, or unique projects are a plus.
- NATO-aligned nationality or close ally citizenship is required.
Why us?
Join us to shape the future of AI-driven defense!