Founding Machine Learning Engineer (Recommendations + GenAI)

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:

• A 30-minute interview with a member of our HR team to get to know you and your experience;
• A 1-hour technical interview;
• A final interview to gauge your fit with our culture and working style.

Sounds interesting?

Do not hesitate to apply or contact us if you have any questions!