Data & Machine Learning Engineer (All genders)
Summary
Owns data infrastructure and ML model development for the Operations Excellence team at a defence-tech company building autonomous unmanned systems. Designs data pipelines from MES/ERP and back-office sources, develops ML models (yield prediction, anomaly detection), and deploys them into production with monitoring and retraining workflows.
STARK is a new kind of defence technology company revolutionising the way autonomous systems are deployed across multiple domains. We design, develop, and manufacture high-performance unmanned systems that are software-defined, mass-scalable, and cost-effective — providing operators with a decisive edge in contested environments.
We are focused on delivering deployable, high-performance systems — not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe, today.
As Data & Machine Learning Engineer, you own the data infrastructure and ML model development for the OAA team's AI use cases. You build the pipelines that feed models with clean, reliable data from both operational systems and back-office sources, deploy models into production, and ensure they perform reliably — from yield prediction on the line to anomaly detection in financial data.
Design and build data pipelines from operational (MES, ERP) and back-office sources feeding ML models
Develop ML models for production and back-office use cases — from experimentation through to production deployment
Deploy models into production: serving infrastructure, monitoring, drift detection, and retraining workflows
Work with the OAA Lead and stakeholders to scope and validate ML use cases — feasibility, data availability, ROI
Collaborate with the Automation Engineer to integrate model outputs into automated workflows
Maintain and improve deployed models as data distributions and operational conditions evolve
Document data pipelines, model architectures, feature definitions, and deployment configurations
4–7 years in data engineering or ML engineering
Demonstrated experience deploying ML models to production: not just research or notebook-level work
Python: core language for data engineering and ML development
SQL: data extraction, validation, and pipeline development
ML frameworks: scikit-learn, PyTorch, or equivalent
MLOps fundamentals: model versioning, serving, monitoring, retraining
MSc in Data Science, Computer Science, Statistics, or equivalent
Data pipeline tooling: Airflow, dbt, or equivalent
Cloud data platforms: AWS, GCP, or Azure
Experience with industrial, time-series, or back-office financial data