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. You will work closely with ML and perception teams, focusing on
building, curating, and continuously improving the datasets behind object detection —
turning real-world data into reliable, high-performing systems.- Design and maintain high-throughput, scalable pipelines to ingest and organize large volumes of time-series camera and sensor data (RGB, IR, thermal, acoustic, depth, IMU)
- Own, curate, and continuously improve computer vision datasets for object detection and classification, ensuring high-quality, diverse, and statistically representative data
- Build and operate active learning loops to prioritize high-value samples and accelerate dataset improvements
- Write robust preprocessing and transformation pipelines using Python, NumPy, Pandas, and Albumentations for large-scale computer vision workloads
- Manage labeling workflows, including automation, QA validation, annotation consistency checks, and dataset versioning
- Collaborate with ML Engineers to fine-tune, train, and evaluate detection models, feeding insights back into data generation and selection
- Analyze model weaknesses, blind spots, bias, and drift to derive actionable data improvements
- Create internal tools and dashboards to visualize, audit, and analyze dataset quality, diversity, long-tail distributions, and model 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!