Senior Machine Learning Engineer- Identity & Trust

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.

Responsibilities

• 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

Requirements

• 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)

Qualifications

• 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

Skills

• Strong understanding of machine learning algorithms and techniques
• Experience with large-scale data processing and analysis
• Knowledge of cloud computing and distributed systems
• Excellent problem-solving and analytical skills
• Ability to work collaboratively in a fast-paced, agile environment

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