Founding Machine Learning Engineer (Recommendations + GenAI)

Join Whizdom.ai as a Founding Machine Learning Engineer to design and build the core intelligence behind everything the company create.
About the company:
Whizdom AI is an early-stage AI startup building products around recommendation systems, personalisation, and GenAI agents. The company is a small team working directly on real customer problems, shipping quickly, measuring outcomes, and iterating fast. Everyone here is expected to take ownership, improve systems proactively, and help build the engineering foundations the company will scale on.
The Role:
We are looking for a Founding Machine Learning Engineer to design, build, and improve the machine learning systems behind Whizdom AI products.
You will own the intelligence layer of the platform, working on recommendation systems, personalisation, ranking, retrieval, GenAI workflows, and predictive analytics solutions. You will collaborate closely with founders, backend engineers, and product teams to transform business problems into ML solutions that deliver measurable customer value.
This is an end-to-end ML engineering role combining data exploration, modelling, experimentation, evaluation, and production iteration. The focus is not only on developing models but also on building reliable ML systems that can be deployed and improved in real-world products.
Key Responsibilities:
• Design, develop, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows;
• Translate product goals into ML problems, evaluation approaches, experiments, and production solutions;
• Analyse behavioural, transactional, contextual, and unstructured data to identify patterns and improve model performance;
• Develop offline evaluation frameworks and support online experiments to measure model quality and business impact;
• Improve GenAI workflows through retrieval, context management, prompting, tool usage, orchestration, and evaluation approaches;
• Perform error analysis and investigate model limitations to improve reliability and performance;
• Collaborate with backend and platform engineers to deploy, monitor, and iterate on ML solutions in production;
• Define ML metrics, experimentation practices, and technical standards;
• Build reusable ML components and maintain clean, testable Python code;
• Support predictive analytics initiatives, including segmentation, churn prediction, opportunity ranking, and other data-driven solutions.
Required Skills & Experience:
• 5+ years of experience building and shipping ML systems or intelligent product features;
• Strong foundations in machine learning, statistics, computer science, or a related quantitative field;
• Strong Python programming skills and experience working with ML workflows;
• Experience developing and evaluating machine learning models in production or near-production environments;
• Good understanding of model evaluation, feature engineering, experimentation, and data quality challenges;
• Experience working with behavioural, transactional, contextual, or large-scale datasets;
• Strong software engineering practices, including writing clean, testable, and maintainable code;
• Ability to work independently, communicate clearly, and solve ambiguous technical problems;
• Upper-Intermediate English level or higher.
Nice to Have:
• Experience with recommendation systems, ranking, search, personalisation, or marketplace optimisation;
• Experience with LLM applications, RAG, GenAI agents, prompt engineering, or GenAI evaluation;
• Experience running A/B tests and online experiments;
• Experience with real-time ML systems, streaming features, low-latency inference, or online learning;
• Experience with causal inference, uplift modelling, multi-armed bandits, or optimisation methods;
• Experience with cloud ML infrastructure, containerised deployment, and MLOps workflows;
• Experience in iGaming, fintech, e-commerce, or other domains with behavioural and transactional data;
• Experience with predictive analytics use cases such as segmentation, churn prediction, LTV modelling, or opportunity prioritisation.
Gross monthly compensation: EUR 5,000–10,000 (B2B contract).
The final offer depends on:
• Relevant professional experience;
• Technical assessment results;
• Interview performance;
• Overall alignment with the role requirements.

Benefits:
• Direct access to the founders and the opportunity to influence platform and engineering decisions;
• High-ownership role with the opportunity to build production foundations from the early stages of the company;
• Opportunity to work on recommendation systems and GenAI products used by real customers;
• Flexible remote environment with strong overlap with European time zones preferred;
• Small team environment with low bureaucracy and significant impact on product development.
Interview Process:
• 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.
Equal Opportunity Statement:
Employment decisions are based on qualifications, skills, experience, and business needs without regard to gender, age, ethnicity, religion, disability, sexual orientation, or any other protected characteristic.
Data Privacy:
Personal data submitted during the recruitment process will be processed solely for recruitment purposes and in accordance with applicable data protection legislation, including the General Data Protection Regulation (GDPR).

If you find this opportunity right for you, don't hesitate to apply or get in touch with us if you have any questions!