Product ML Engineer

Role Description

We're SweedPos, a product-driven startup building an all-in-one cannabis retail platform. We’re on the lookout for a Senior ML Engineer to join us remotely and help us build recommendation and personalization systems across our eCommerce ecosystem.

You’ll work primarily within our eCommerce domain, helping us build the next generation of recommendation and personalization capabilities. Our long-term goal is to build a shopping experience that adapts to each customer. This is a particularly interesting stage to join because many foundational decisions are still ahead of us.

What You’ll Do

• 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.

What You’ll Be Working On

• Evolving our existing recommendation engine.
• Building deeper customer-level personalization.
• Personalized product ranking and content selection.
• Dynamic homepage and carousel composition.
• Next-best-action models.
• Recommendation-powered conversational shopping experiences.
• AI-powered product search.
• Customer behavior and demand forecasting.
• Improving the data and feature pipelines behind ML systems.
• Building better experimentation and evaluation workflows.

Qualifications

• 5+ years of production ML / Machine Learning Engineering experience.
• Strong commercial experience with recommendation systems.
• Strong Python and SQL skills.
• Experience working with ranking, retrieval, candidate generation, collaborative filtering, embeddings, learning-to-rank, or similar recommendation approaches.
• Experience building and maintaining production ML systems.
• Experience working with offline ML metrics and online product/business metrics.
• Experience with A/B testing and experimentation.
• Strong understanding of the full ML lifecycle: experimentation, deployment, monitoring, and iteration.
• Experience building APIs or production inference services.
• Good understanding of data pipelines and working with 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 help shape the right solution.

Nice to Have

• Experience with forecasting or time-series models.
• Experience with demand forecasting or customer behavior prediction.
• Experience with eCommerce, marketplaces, advertising, food delivery, or other recommendation-heavy products.
• Experience with personalization systems.
• Experience with search or information retrieval.
• Experience with data engineering or data modeling.
• Experience building feature pipelines, feature stores, or metric layers.
• Experience developing internal ML tooling or ML platforms.
• Experience with model serving or inference optimization.
• Experience building ML systems from an early stage rather than only maintaining an established stack.

What We Offer

• Salary in USD (B2B contract with the US company).
• 100% remote - We’re a remote-first company, no offices needed!
• Flexible working hours - Core team time: 10:00-16:00 CET (flexible per team).
• 20 paid vacation days + 12 holidays per year.
• 3 sick leave days.
• Medical insurance after probation.
• Equipment reimbursement (laptops, monitors, etc.).

Hiring Process

• Recruiter Call (45 minutes): Introduction, role overview, experience discussion, and English check.
• Experience Deep Dive (60 minutes): A detailed discussion of one of your most relevant production ML projects.
• ML System Design (60 minutes): A practical architecture discussion based on a problem similar to the ones you could work on at Sweed.
• Final Interview (60 minutes): A conversation with the ML and eCommerce leadership focused on product thinking, ownership, cross-functional collaboration, communication, and overall fit for the role.