Senior Machine Learning Engineer- Identity & Trust

Job Description:

About the Role

The Trust Machine Learning team protects Remitlys 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 Remitlys 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 teams scientific bar through experiment design reviews model deep-dives mentoring and bringing relevant external research into the teams 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 $196000-$$245000. 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 teams 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.