Data & Machine Learning Engineer (All genders)

Job description

At Stark, we forge the future of European and national security through cutting-edge, AI-driven technology and unmatched engineering precision. As a premier defence technology company, our mission is clear: to equip NATO Allies and their partners with next-generation autonomous defence platforms and advanced AI-driven software built to perform in the world’s most demanding environments. We believe that true security stems from relentless innovation. By combining elite engineering talent with agile technology and an AI-native operational culture, Stark turns complex defence challenges into field-tested, multi-domain solutions. Why Join Us? The software and systems you work on directly protect personnel and defend European and national sovereignty. You will work alongside industry-leading minds on autonomous systems, advanced propulsion, and secure cyber platforms. We foster a fast-paced, collaborative environment where bold ideas are supported by serious R&D backing. About the Team The Operations Excellence team sits within the COO organization and serves as a strategic partner to managers, team leads, and colleagues across Stark. By delivering data-driven insights, leading critical projects, and driving continuous process improvement, we help the organization operate more efficiently, scale effectively, and achieve its goals faster. As an individual contributor, you will take end-to-end ownership of complex initiatives with significant business impact. Working closely with cross-functional stakeholders, you will have the opportunity to influence key decisions, shape core operating processes, and contribute directly to the success of one of Europe’s fastest-growing unicorns. Your Mission 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. Responsibilities 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, monit…