Machine Learning Engineer

Hi There!

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.

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 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.

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.

Why This Role Matters

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.

What To Do In The Project?

• Design and run experiments to test hypotheses, validate new ideas, and identify opportunities to improve product performance through data-driven insights.
• Develop, train, and deploy machine learning models that power features such as intelligent recommendations, personalization, and demand forecasting.
• Work across the ML lifecycle - from exploratory analysis and feature design to experimentation, evaluation, and scalable production deployment.
• Collaborate closely with product manager and engineers to define measurable hypotheses, set success metrics, and translate business problems into data experiments.
• Prototype rapidly, validate results through rigorous testing, and productionize successful models and algorithms with robust engineering practices.
• Establish and evolve experimentation frameworks - from offline evaluation pipelines to A/B testing setups and post-launch monitoring.
• Mentor teammates on experimental design, statistical rigor, and best practices for balancing exploration with production reliability.
• Stay ahead of new research and techniques in machine learning, generative AI, and applied data science, continuously bringing innovative ideas into production.

What Professional Skills Are Important For Us?

• 4+ years of hands-on experience in applied machine learning, data science, or ML engineering - ideally spanning both experimental research and production deployment.
• Proficiency in Python and SQL, with strong knowledge of ML frameworks and experimentation libraries.
• Experience designing and running experiments (e.g., A/B tests, offline evaluations, hypothesis validation).
• Strong ML engineering skills - comfortable building and maintaining scalable ML pipelines and deploying models to production.
• Fluency in MLOps practices, including model monitoring, CI/CD, and automation for ML workflows.
• Proven ability to translate ambiguous business challenges into structured experiments and measurable outcomes.
• Collaborative mindset, with a balance of scientific curiosity and pragmatic engineering discipline.
• Strong communication and storytelling skills - able to articulate findings, influence decisions, and connect experimentation outcomes to business impact.

What Else Matters?

• 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
• 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

What We Offer

• 3 sick leave days
• Medical insurance after probation
• Equipment reimbursement (laptops, monitors, etc.)
• Recruiter Call (up to 45 minutes)
• Technical Interview (up to 1.5 hour)
• Machine Learning System Design (up to 1 hour)
• Final Interview with Head of Data (up to 1 hour)
• 3 sick leave days
• Medical insurance after probation
• Equipment reimbursement (laptops, monitors, etc.)
• Recruiter Call (up to 45 minutes)
• Technical Interview (up to 1.5 hour)
• Machine Learning System Design (up to 1 hour)
• Final Interview with Head of Data (up to 1 hour)

Hiring Process