Senior Machine Learning Engineer (Recommendation Systems)

TL;DR

Senior Machine Learning Engineer (Recommendation Systems): Building and improving production recommendation, ranking, and personalization systems for an eCommerce cannabis retail platform with an accent on candidate generation, retrieval, experimentation, and scalable inference. Focus on designing ML systems from an early stage, connecting model quality to product metrics, and building reliable feature pipelines, APIs, monitoring, and tooling.

Location: 100% remote internationally with no office attendance required. Core collaboration hours are 10:00–16:00 CET, with flexibility by team.

Company

hirify.global is a product-driven startup building an all-in-one cannabis retail platform combining POS, eCommerce, marketing, analytics, and inventory management.

What you will do

• Build and improve production recommendation, ranking, retrieval, and personalization systems across the eCommerce customer journey.
• Design personalized product feeds, carousels, content ordering, next-best-action models, and conversational shopping experiences.
• Develop scalable inference services, ML APIs, feature pipelines, training workflows, monitoring, and internal ML tooling.
• Define offline ML and online product metrics, design experiments, and run A/B tests to validate product hypotheses.
• Collaborate with Product, Engineering, Analytics, Data Platform, and backend teams to translate business problems into measurable ML systems.
• Shape architecture and technical decisions while building foundational recommendation and personalization capabilities.

Requirements

• 5+ years of production machine learning or Machine Learning Engineering experience, including strong commercial experience with recommendation systems.
• Strong Python and SQL skills, plus experience with ranking, retrieval, candidate generation, collaborative filtering, embeddings, or learning-to-rank.
• Experience building and maintaining production ML systems across experimentation, deployment, monitoring, and iteration.
• Experience with offline and online metrics, A/B testing, production inference services, data pipelines, behavioral or transactional data, and ML APIs.
• Familiarity with MLOps, CI/CD, observability, production reliability, and strong software engineering practices.
• Ability to work independently in ambiguous environments, take ownership of outcomes, and collaborate directly with Product teams.

Nice to have

• Experience with forecasting, time-series models, demand forecasting, or customer behavior prediction.
• Background in eCommerce, marketplaces, advertising, food delivery, search, or information retrieval.
• Experience with personalization systems, data engineering, feature stores, metric layers, ML platforms, or inference optimization.
• Experience building ML systems from an early stage rather than only maintaining established infrastructure.

Culture & Benefits

• Remote-first work with flexible working hours and core team time from 10:00 to 16:00 CET.
• High ownership over ML initiatives and regular technical discussions and design reviews.
• 20 paid vacation days, 12 holidays, and 3 sick leave days per year.
• Medical insurance after probation and equipment reimbursement for laptops, monitors, and related equipment.
• B2B contract with the US company, with compensation paid in USD.

Hiring process

• 45-minute recruiter call covering the role, experience, and an English check.
• 60-minute experience deep dive focused on a relevant production ML project.
• 60-minute ML system design discussion followed by a 60-minute final interview with ML and eCommerce leadership.