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.

Fraud is one of the hardest applied machine learning problems: an adversary who adapts to every model we ship, extreme class imbalance, labels that arrive late or not at all, and outcomes we only observe for the transactions we approve. Making progress here takes both strong engineering and scientific rigor.

As a Senior Machine Learning Engineer, you will develop the models and systems that detect fraud in real time and push the scientific capabilities of our fraud ML program forward. You will frame ambiguous fraud problems as testable hypotheses, advance our modeling techniques beyond established baselines, and carry your ideas from experimentation through to reliable production systems.

You will report to the Senior Manager of Trust Machine Learning. This role is based in Remitly's office in Seattle, Washington.

You Will:

• Design, build, and own machine learning models and systems that identify risky transactions and behavior in production
• Advance our modeling capabilities with techniques suited to adversarial domains (e.g., graph-based methods for fraud rings, sequence models for behavioral patterns, and semi-supervised or anomaly detection approaches for novel attacks)
• Design rigorous offline and online evaluation methodologies that account for extreme class imbalance, delayed and censored labels, selective labeling bias, and adversarial drift
• Take modeling work from prototype to production, ensuring models meet real-time latency, reliability, and monitoring requirements
• Raise the team's scientific bar through experiment design reviews, model deep-dives, mentoring, and bringing relevant external research into the team's practice
• Collaborate with data scientists, risk operations, and business stakeholders to identify emerging fraud patterns and translate them into modeling opportunities

You Have:

• A degree in computer science, machine learning, statistics, or a related quantitative field (advanced degree preferred), or equivalent experience
• 5+ years of experience building and deploying machine learning systems, including a track record of taking novel modeling approaches from idea to production
• Deep grounding in ML fundamentals and experimental design: you can reason about why a model works, not just whether it does, and design evaluations that hold up under distribution shift
• 5+ years of programming experience in Python or equivalent, with hands-on experience in modern ML frameworks (e.g., PyTorch, XGBoost/LightGBM, scikit-learn)
• Experience working with cloud platforms (e.g., AWS, GCP, Azure)

Nice to Have:

• Experience in fraud, risk, abuse, trust and safety, or another adversarial ML domain
• Publications, patents, or open-source research contributions demonstrating applied research impact
• Depth in one or more of: graph machine learning, sequence/behavioral modeling, anomaly detection, causal inference, or LLM applications to risk.

Compensation Details. The starting base salary range for this position is typically $196,000-$$245,000. In the U.S., 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:

• Flexible paid time off
• Health, dental, and vision + 401k plan with company matching
• Paid parental, medical, military and family care leave
• Mental Health & Family Forming Benefits
• Employee Stock Purchase Plan (ESPP)
• Continuing education

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.