Machine Learning Engineer

Job Description:

At Remitly, we believe everyone deserves the freedom to access, move, and manage their money wherever life takes them. Since 2011, we've tirelessly delivered on our promise to customers sending money globally, providing secure, simple, and reliable ways to manage their money, ensuring true peace of mind. Whether it's supporting loved ones back home, growing a business across continents, or pursuing new opportunities abroad, we're not just here to move money— we're here to move our global customers forward.

We're looking for builders, reimaginers, and global thinkers who want to work at the intersection of technology, trust, and transformation. If that's you and you're ready to do the most meaningful work of your career—we invite you to join over 2,800 passionate Remitlians worldwide who are united by our vision to transform lives with trusted financial services that transcend borders.

About the Role:

The Trust Machine Learning team protects Remitly's customers by developing intelligent systems that prevent fraud. These systems assess the risk of every transaction and customer interaction, while minimizing the impact on user experience and customer trust.

As a Machine Learning Engineer, you will build feature engineering pipelines by integrating with rich data sources, optimize model performance by exploring new techniques and algorithms, and ensure the quality of live production models.

You Will:

• Design, implement, and maintain machine learning models and systems to identify risky transactions and behavior
• Build scalable, reliable feature engineering pipelines for batch, streaming, and real-time features consumed by a diversity of machine learning models
• Optimize models and pipelines, ensuring system reliability and model monitoring by applying MLOps best practices
• Contribute to design reviews, architectural discussions, code reviews, and other technical activities to improve team practices and systems

You Have:

• A degree in computer science, data science, statistics, or related field (or equivalent experience)
• 2+ years of experience building and deploying machine learning and/or data-intensive systems
• 2+ years of programming experience in Python, Go, Scala, or equivalent
• Hands-on experience with ML frameworks (e.g., PyTorch, scikit-learn, numpy)
• Experience working with cloud platforms (e.g., AWS, GCP, Azure)

Compensation Details. The starting base salary range for this position is $136,000-$170,000. In Canada, Remitly employees are shareholders in our Company and equity is part of our total compensation plan. Your recruiter can share more information about medical benefits offered, as well as other financial benefits and total compensation components offered with this role.

Our Benefits

• Four weeks vacation
• Health Benefits
• Mental Health & Family Forming Benefits
• RRSP plan with company match
• Employee Stock Purchase Plan (ESPP)
• Life Insurance & Disability
• Continuing education and travel benefits

Our Connected Work Culture: Driving Innovation, Together

At Remitly, we believe that true innovation sparks when we come together. Our Connected Work Culture fosters dynamic in-person collaboration, where ideas ignite and challenging problems find solutions faster. For corporate team members, we have an in-office expectation of at least 50% of the time monthly, typically achieved by coming in three days a week. This creates a consistent, meaningful overlap that supports team norms and business needs. Managers also have the flexibility to set higher expectations based on their team's specific needs. These intentional in-office moments are vital for deepening relationships, fueling creativity, and ensuring your impact is felt where it matters most.

Remitly is an E-Verify Employer

At Remitly, we are dedicated to ensuring that our workplace offers equal employment opportunities to all employees and candidates, in full compliance with applicable laws and regulations.

Remitly is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.