Rain is hiring a Machine Learning Engineer - Fraud Risk in Engineering — New York, NY. The overview below is synthesized from the employer posting on jobs.ashbyhq.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
About the Company
Company context from the listing:
Rain is the global stablecoin payments platform for enterprises, neobanks, platforms, developers, and AI agents. Our technology allows partners to move, store, and use stablecoins instantly and compliantly through global payment cards, rewards, on/offramps, wallets, and cross-border rails.
You will have the opportunity to deliver massive impact at a hypergrowth company backed by some of the top investors in fintech, crypto, and SaaS. In January 2026, we closed a $250M Series C led by ICONIQ, valuing Rain at $1.95B, with Sapphire Ventures, Dragonfly, Bessemer Venture Partners, Galaxy Ventures, FirstMark, Lightspeed, Norwest, and Endeavor Catalyst also participating.
Our Ethos
We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals.
About the Team
Company context from the listing:
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.
What you’ll do
Day-to-day scope for the Machine Learning Engineer - Fraud Risk as described in the posting:
- 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
What we're looking for
- 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, TensorFlow, 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
Nice to have, but not mandatory
Additional experience noted as a plus:
- Domain expertise in banking, payments, or transaction monitoring
- Experience with graph-based or network-level fraud detection techniques
- A graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field
- Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation (in partnership with data science)
- Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts
Things that enable a fulfilling, healthy, and happy experience at Rain
Unlimited time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves.
Flexible working ☕ We support a flexible workplace – work from home, come into an office, or both. We want everyone to work in an environment where they're their most confident and productive selves.
Easy to access benefits 🧠 For US Rainmakers, we cover 95% of your health, dental, and vision plan costs and 90% for your dependents, plus a 100% company-subsidized life insurance plan.
Retirement goals💡 Plan for the future with confidence. We offer a 401(k) with a 4% company match.
Equity plan 📦 Every Rainmaker gets an equity option plan so we all benefit from our success.
Health and Wellness 📚 High performance begins from within. Rainmakers receive a monthly stipend to be used for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you!
In-office meals 🍜 Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit.
Team summits ✨ Summits play an important role at Rain. Time together helps us build relationships and a common destiny.