Machine Learning Engineer

Descripción del puesto

About ExTrac ExTrac is a decision intelligence company used by public and private sector organisations operating in complex, fast-moving environments. Our capabilities fuse curated data sources, domain-specific AI, and deep human expertise to transform information overload into clear, actionable foresight. Our ambition is to become the analytical backbone that organisations rely on when geopolitical uncertainty becomes an opportunity or a strategic risk. More at extrac.ai . About the Role We are currently only able to consider applicants who are nationals of a NATO member state, Australia, or New Zealand. This role is open at both mid and senior level. Scope, autonomy and expected impact will scale with experience and ability. Senior candidates will typically lead technical direction on major initiatives, set standards across the team and mentor others; mid-level candidates will own significant components end-to-end and contribute to broader technical decisions. The Machine Learning team owns ExTrac’s modelling capabilities end-to-end. You will partner directly with software engineering and product design to understand requirements, design and run model evaluations, integrate optimised workflows into production pipelines and ensure results remain reliable at scale. You will architect cutting-edge agentic systems for large-scale analysis of the geopolitical landscape, train and evaluate domain-specific models for the unique problems our customers face and work with design and engineering to bring them into the product. Alongside domain experts you will translate research into practical solutions for analysts operating at the frontier of their field. In this role you will set high technical standards and take projects from prototype to reliable production deployment. You will collaborate closely with the engineers who design the data collection and processing backend and engage directly with senior stakeholders. We look for people who enjoy challenging problems, keep