Machine Learning Engineer - Fraud Risk , NY, Remote

Location

New York, NY

Full time

Hybrid

Engineering

• $187K – $258.7K
  • Offers Equity
  • Offers Bonus

Rain makes the next generation of payments possible across the globe. We’re a lean and mighty team of passionate builders and veteran founders. Our infrastructure makes stable coins usable in the real-world by powering card transactions, cross-border payments, B2B purchases, remittances, and more. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient.

You will have the opportunity to deliver massive impact at a hypergrowth company that is funded by some of the top investors in fintech, crypto, and SaaS, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and several more. If you’re curious, bold, and excited to help shape a borderless financial future, we’d love to talk.

We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company.

The fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect our customers and deliver a low-friction experience. We achieve this by maintaining transaction and lifecycle event monitoring, building alerts to speed fraud detection and response, and creating risk rules and strategies powered by ML models. We are a pillar of the business, supporting new products and ensuring their success.

Rain’s next-generation payment technology introduces new fraud vectors that require holistic, end-to-end thinking, strong data fundamentals, and fraud management savvy to combat.

• Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis

• Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring

• Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams

• Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback)

• Build robust monitoring and alerting for model performance, latency, data quality, and drift

• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases

• Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle

• Partner with platform teams to meet strict SLAs for availability, latency, and accuracy

• Collaborate closely with talented engineers, data scientist and compliance teams across Rain

• Work in a fast-paced environment on a rapidly growing product suite

• Solve complex problems at the intersection of ML systems, data, and reliability

• 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains

• A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field

• Proven track record designing and maintaining ML models at scale

• Advanced proficiency in Python and ML frameworks (e.g., PyTorch, Tensor Flow, scikit-learn)

• Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling

• Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems

• Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques

• Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale

• Domain expertise in banking, payments, or transaction monitoring

• Experience with graph-based or network-level fraud…