Are you ready to take your Machine Learning skills to the next level?
We are hiring a Founding Machine Learning Engineer to own the intelligence layer of our products. You will design, build, and improve the models and decision systems behind recommendations, ranking, personalisation, retrieval, agent behaviour, and selected predictive analytics use cases. You will work directly with the founders to turn ambiguous product ideas into production systems that create measurable customer value. In a team of our size, this is an end-to-end role: you may touch data exploration, modelling, evaluation, experimentation, and production iteration in the same week.
This is not a pure research role. We care about people who can move from data and hypotheses to shipped systems and business impact.
All you need is:
Strong foundations in machine learning, statistics, computer science, or a similar quantitative discipline;
Experience building and shipping ML systems or intelligent product features in production or near-production environments;
Strong Python skills and comfort working across data, modelling, evaluation, and production collaboration;
Good understanding of experimentation, model evaluation, feature engineering, data quality, and error analysis;
Clear communication and the ability to work through messy, ambiguous product problems;
High ownership, self-direction, and a strong bias toward action;
5+ years building and shipping ML systems or intelligent product features in production;
Strong understanding of model evaluation, cross-validation, feature engineering, and data quality challenges in real-world environments;
Experience working with large-scale behavioural, transactional, or contextual data;
Strong software engineering habits, including writing clean, testable, maintainable Python code.
What would be an advantage:
Experience with recommendation systems, ranking, search, personalisation, or marketplace/feed optimisation;
Experience with LLM applications, RAG, GenAI agents, prompt iteration, or evaluation of GenAI systems;
Experience running A/B tests or online experiments;
Experience working closely with product teams and translating user problems into ML solutions;
Experience with real-time ML, streaming features, low-latency inference, or online learning;
