Product ML Engineer - Sweed

Hi there!

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.

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 (8–12 members, including front-end/back-end developers, QA specialists, and analysts).
• Each team is led by a Team Lead and a Product Owner.
• Our CEO, account managers, and customer success team are based in the USA, working closely 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.

You’ll have the opportunity to influence the architecture, tooling, experimentation approach, data requirements, and overall direction of our recommendation systems from an early stage.

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 behavioral and transactional data availability.
• 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 data and feature pipelines behind ML systems
• Building better experimentation and evaluation workflows

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 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 behavioral/transactional data.
• Familiarity with MLOps, CI/CD, observability, and production reliability.
• Strong software engineering fundamentals and ability to work independently.
• Strong communication skills and collaborative mindset.

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 recommendation-heavy products.
• Experience with personalization systems, search, or information retrieval.
• Experience with data engineering, data modeling, 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.

What We Offer

• Salary: Compensation paid in USD via B2B contract with a US entity
• Location: 100% Remote
• Schedule: Flexible working hours with core team overlap around 10:00–16:00 CET
• Vacation & Holidays: 20 paid vacation days + 12 holidays per year
• Sick Leave: 3 sick leave days per year
• Health Care: Medical insurance coverage after probation
• Equipment: Reimbursement for work setup (laptops, monitors, etc.)

Hiring Process

1. Recruiter Call (45 mins): Introduction, role overview, experience discussion, and English check.
2. Experience Deep Dive (60 mins): Discussion of a relevant production ML project covering context, architecture, technical trade-offs, metrics, and experimentation.
3. ML System Design (60 mins): Practical architecture discussion focused on recommendations, personalization, or forecasting.
4. Final Interview (60 mins): Conversation with ML and eCommerce leadership on product thinking, ownership, collaboration, and overall fit.