At Alpine Eagle, we are a team of specialists with backgrounds in defence, aerospace engineering and machine learning, building the first airborne, swarm-based system to provide protection against the rising drone threat. We believe that recent conflicts have shown the malicious and disruptive potential of drones in both civilian and defense scenarios. In addition, we are convinced that the next step in counter-drone is air-to-air. We want to ensure that all governments and institutions that believe in personal freedom and the rule of law have cutting edge solutions to protect against this rising threat.

We offer a work environment that is inclusive and open, with a flat hierarchy, and a purpose-driven team. We want to reinforce a culture where great ideas can flourish, and great people collaborate.

How you will make an impact:

We’re looking for someone to take ownership of our data and model lifecycle: from sourcing and curating datasets to maintaining the infrastructure and tooling that make experimentation and deployment reproducible and robust.

You’ll sit at the heart of our AI team and help us build the scaffolding that powers everything else, ensuring we can go from raw data to trained models to production systems with confidence, traceability, and speed.

You won’t just be building infrastructure. You’ll be actively contributing to decisions about how data is collected, labeled, processed, and used to drive model performance. You’ll work closely with ML researchers, roboticists, and test teams to ensure our datasets are high-quality, evolving, and aligned with real-world constraints.

Your day-to-day:

• Own the end-to-end data pipeline: ingest real flight logs and simulated sensor data, enforcing shared schemas, preprocessing, and quality checks.
• Design and operate streaming & batch flows: supply both real-time systems and offline analytics at scale.
• Work hand-in-hand with ML researchers and test engineers: gather new data, define labeling guidelines, and expand dataset coverage.
• Govern datasets and experiments: manage versioning, metadata, and full reproducibility with tools such as DVC, MLflow, Weights & Biases.
• Maintain and extend our MLOps foundation: cover everything from training orchestration to deployment monitoring, ensuring reliability, security, and clear observability throughout the model lifecycle.
• Use containerization (e.g., Docker): package and deploy components reproducibly across environments.

✅ What you need to be successful:

• Hold an MSc (or higher): in data engineering, computer science, machine learning, robotics, or a closely related field, and have practical experience building end-to-end data pipelines, versioned datasets, and MLOps workflows for production AI systems.
• Have a solid record of transforming real-world data: you have handled heterogeneous logs, sensor streams, or image/video collections and converted them into reliable, well-documented datasets.
• Write high-quality Python and work confidently with diverse data formats: JSON, Parquet, NumPy, as well as domain-specific formats such as image/video files and ROS bags.
• Apply modern data-and-ML tooling effectively: experienced with (or able to master) tools such as DVC, MLflow, Weights & Biases, Airflow, or their equivalents.
• Operate comfortably in Linux environments: proficient with the command line, git, and shell scripting.
• Design clean, precise architectures with thorough documentation: deliver scalable, maintainable data and MLOps solutions and capture the design rationale for the team.

At Alpine Eagle, we are committed to diversity and equal opportunity. We welcome applications from all individuals regardless of ethnic origin, gender, religion, disability, age, or sexual identity.