Founding Machine Learning Engineer (Recommendations + GenAI)

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;
Experience with causal inference, uplift modelling, multi-armed bandits, or other decision-optimisation methods;
Familiarity with cloud ML infrastructure, containerised deployment, and MLOps workflows;
Experience in iGaming, fintech, e-commerce, or another domain with large-scale transactional and behavioural data;
Experience with predictive analytics use cases such as segmentation, churn prevention, LTV modelling, or opportunity prioritisation.
Your daily adventures will look like:
Design, build, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows;
Turn product goals into concrete ML problems, evaluation plans, experiments, and shipped features;
Work with behavioural, transactional, contextual, and unstructured data to identify signals and improve model quality;
Build offline evaluation frameworks and online experiments to measure relevance, quality, latency, cost, and business impact;
Improve GenAI agent behaviour through better retrieval, context management, prompting, tool use, orchestration, and evaluation;
Investigate failure modes, run error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity;
Partner closely with platform and backend engineers to deploy, monitor, and iterate on models in production;
Help define how the company does ML: metrics, experimentation discipline, technical standards, and long-term direction;
Work with real-time behavioural and transactional signals to improve recommendations, personalisation, and intelligent product behaviour;
Contribute to predictive and insight-driven ML use cases such as segmentation, churn prediction, recommendation measurement, and opportunity ranking;
Write clean, testable Python and contribute reusable ML components and shared libraries used across the platform.
What Success Looks Like in the First 6 Months:
You ship meaningful improvements to a recommendation, personalisation, or GenAI workflow used in production;
You establish a practical evaluation framework for one or more core ML systems;
You turn ambiguous product opportunities into clear experiments and sound technical decisions;
You improve at least one metric that matters, such as relevance, task completion, conversion, retention, latency, or cost efficiency;
You become a trusted owner who spots high-leverage ML opportunities and drives them forward without needing detailed instruction;
You help establish a repeatable approach to experimentation, model iteration, and production-quality ML development.
And this is how our interview process goes:

Employment type
Full-Time

Work arrangement
Yes