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!

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Compensation & Other Benefits

SalaryNegotiable

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
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 improv
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

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

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