machine learning engineer for eCommerce personalization

SweedPos is a product-driven startup building an all-in-one cannabis retail platform that combines POS, eCommerce, Marketing, Analytics, and Inventory Management. Its enterprise-grade solution helps cannabis retailers improve operational efficiency, accessibility, compliance, and business growth.

• Build and improve production recommendation and ranking systems;
• Develop personalization models across different parts of the eCommerce customer journey;
• Work on candidate generation, retrieval, ranking, and re-ranking approaches;
• Design personalized product feeds, carousels, content ordering, and next-best-action experiences;
• Contribute to customer behavior, demand, and product-level forecasting use cases;
• Connect recommendation systems with search and conversational shopping experiences;
• Define and track offline ML metrics and online product metrics;
• Design and run experiments and A/B tests to validate product hypotheses;
• Build scalable inference services and ML APIs;
• Improve feature pipelines, training workflows, monitoring, and internal ML tooling;
• Work closely with Data Platform and backend teams to ensure the right behavioral and transactional data is available;
• Participate in architectural discussions and technical decision-making;
• Help Product teams translate business problems into measurable ML problems;
• Take responsibility for outcomes by understanding problems, identifying dependencies, driving technical solutions, deploying them, and following results;
• Help shape technical foundations for faster, more measurable, and scalable ML development;
• Contribute to the evolving roadmap, systems, and processes in an ambiguous environment.

• 5+ Years of production ML / Machine Learning Engineering experience;
• Strong commercial experience with recommendation systems;
• Strong Python and SQL skills;
• Experience with ranking, retrieval, candidate generation, collaborative filtering, embeddings, learning-to-rank, or similar recommendation approaches;
• Experience building and maintaining production ML systems;
• Experience with offline ML metrics and online product or business metrics;
• Experience with A/B testing and experimentation;
• Strong understanding of the full ML lifecycle, including experimentation, deployment, monitoring, and iteration;
• Experience building APIs or production inference services;
• Good understanding of data pipelines and behavioral or transactional data;
• Familiarity with MLOps, CI/CD, observability, and production reliability;
• Strong software engineering fundamentals;
• Experience making technical decisions and taking ownership of solutions;
• Ability to work independently in ambiguous environments;
• Strong communication skills and a collaborative mindset;
• Ability to work directly with Product teams, clarify requirements, challenge assumptions, and shape solutions;
• Ownership of outcomes rather than only implementing predefined tasks;
• Product thinking focused on product and business metrics;
• Ability to explain constraints clearly, challenge requirements constructively, and turn broad ideas into concrete ML problems;
• Curiosity, pragmatism, and adaptability in evolving ML environments;
• Nice to have: forecasting or time-series models, demand forecasting or customer behavior prediction, eCommerce or recommendation-heavy products, personalization systems, search or information retrieval, data engineering or data modeling, feature pipelines or feature stores, internal ML tooling or ML platforms, model serving or inference optimization, building ML systems from an early stage.

• Salary paid in USD under a B2B contract with the US company;
• Flexible working hours with core team time from 10:00-16:00 CET;
• 20 Paid vacation days and 12 holidays per year;
• 3 Sick leave days;
• Medical insurance after probation;
• Equipment reimbursement for laptops, monitors, and other equipment.