Product ML Engineer

About this role

Hi there! We're SweedPos https://sweedpos.com/#/, 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. ABOUT US At Sweed, we’re reimagining how cannabis retailers operate. Our enterprise-grade platform combines POS, eCommerce, Marketing, Analytics and Inventory Management into a single, seamless solution - eliminating the need for multiple third-party tools. We believe in simplicity, efficiency, and innovation. That’s why we build for scalability and performance, making life easier for cannabis retailers while driving real business growth. WHY WE’RE DOING THIS At Sweed, we believe in the medicinal potential of cannabis. It has been shown to help with chronic pain, anxiety, depression, and many other conditions. Despite the lingering stigma, we see cannabis as a powerful tool for improving lives. The industry is evolving rapidly, and we’re here to drive that transformation - making cannabis retail more efficient, accessible, and customer-friendly. WHERE WE ARE NOW We’ve been on the market for 8 years, continuously growing and refining our product. Our focus is on earning customer trust, which means constantly improving our delivery processes and rolling out new features. At the same time, we navigate the complex legal landscape of the cannabis industry, ensuring our platform remains compliant and future-proof. TEAM STRUCTURE Our total team size is over 200 people: The development team is distributed globally and organized into cross-functional product teams. These teams typically consist of 8-12 members, including front-end and back-end developers, QA specialists, and analysts. Each team is led by a Team Lead and a Product Owner, ensuring effective collaboration and clear direction. Meanwhile, our CEO, account managers, and customer success team are based in the USA, working closely with us to align product development with business and user needs. ABOUT THE ROLE You’ll work primarily within our eCommerce domain, helping us build the next generation of recommendation and personalization capabilities. We already have recommendation functionality running in production, including product recommendations and customer-facing eCommerce experiences. At the same time, we’re still at an early stage when it comes to true personalization. Our long-term goal is to build a shopping experience that adapts to each customer - from which products and content they see to how different parts of the journey are ranked, assembled, and presented. This is a particularly interesting stage to join because many foundational decisions are still ahead of us. You’ll have the opportunity to influence the architecture, tooling, experimentation approach, data requirements, and overall direction of our recommendation systems. Unlike in mature recommendation teams, where most of the infrastructure is already established and engineers focus on incremental optimization, here you’ll have the opportunity to build a significant part of the system from the ground up. 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 Some of the initiatives we’re currently exploring include: - 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. The exact roadmap will evolve, and we expect you to actively contribute to shaping it. WHAT WE’RE LOOKING FOR - 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. -