Machine Learning Engineer

We're SweedPos, a product-driven startup building an all-in-one cannabis retail platform. We’re on the lookout for a Machine Learning Engineer to join our team remotely and help us scale and optimize our platform.

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.

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.

We’ve been on the market for 7 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.

Right now, our total team size is around 180 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.

We believe ML can unlock massive value for our customers - from personalized recommendations to predictive analytics. As our ML Engineer, you’ll be part of a small, focused team building the foundation of our ML systems. You’ll work on impactful product features, collaborate with engineers and analysts, and have the freedom to experiment and learn.

• Develop and deploy ML models to power features like product recommendations, personalization, and demand forecasting

• Work across the ML lifecycle - from data exploration and feature engineering to model training, validation, and deployment

• Collaborate closely with product and engineering teams to turn business problems into data-driven solutions

• Design and maintain robust ML pipelines, ensuring models are scalable, testable, and easy to iterate on

• Continuously monitor and improve model performance in production through testing, evaluation, and experimentation

• Contribute to technical decisions, share knowledge with peers, and help evolve best practices in our ML stack

• Stay up to date with recent ML advancements and bring new ideas to the team

• 2+ years of experience as an ML Engineer or Data Scientist

• Strong proficiency in Python and SQL

• Hands-on experience with model training, validation, and deployment

• Familiarity with AWS (especially SageMaker) and Docker is a big plus

• Comfortable working with real-world messy data and iterating quickly

• Understanding of model lifecycle and MLOps principles is a bonus

• Strong communication skills and a desire to work in a fast-paced, product-driven team

• Proactivity – We love team members who take initiative and provide feedback

• Critical thinking – We value problem-solvers who think beyond just writing code

• Adaptability – Our industry is evolving fast, and we need people who thrive in change

• 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: 09:00-15:00 GMT (flexible per team)

• 20 paid vacation days per year

• 12 holidays per year

• 3 sick leave days

• Medical insurance after probation

• Equipment reimbursement (laptops, monitors, etc.)

• Recruiter Call (up to 45 minutes) – Quick intro & optional tech screen

• Hiring Manager call (up to 45 minutes) - Deep dive into your ML background

• Technical Interview (up to 1.5 hours) – Deep dive into ML skills