{"id":29357,"url":"https://alion.io/job/rain-machine-learning-engineer-fraud-risk","title":"Machine Learning Engineer - Fraud Risk","company":{"id":613,"name":"Rain","domain":"rain.xyz","url":"https://alion.io/company/rain","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":90,"open_postings":29,"ghost_share":0,"stale_share":0.207,"repost_share":0,"time_to_fill_p50_days":95,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"TensorFlow","optional":false},{"name":"AI Agents","optional":true},{"name":"Data Augmentation","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-01-09T23:54:18Z","employer_posted_date":"2026-01-09","last_verified_at":"2026-10-17T17:12:26Z","board_verified":true,"closed_at":null,"days_open":280,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":280},"description":"About the Company\nRain 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. As both a Visa and Mastercard Principal Member, Rain issues cards that work at more than 175 million merchant locations in over 220 countries and territories. Built natively for stablecoins and trusted by more than 100 organizations worldwide, Rain delivers secure, scalable infrastructure that makes money move freely and instantly around the world.\nYou 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. If you're curious, bold, and excited to help shape a borderless financial future, we'd love to talk.\nOur Ethos\nWe 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.\nAbout the Team\nThe 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.\nWhat you’ll do\nArchitect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis\n\nDevelop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring\n\nDesign and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams\n\nOwn ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback)\n\nBuild robust monitoring and alerting for model performance, latency, data quality, and drift\n\nLead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases\n\nDevelop tooling and processes to improve the effectiveness and speed of the ML development lifecycle\n\nPartner with platform teams to meet strict SLAs for availability, latency, and accuracy\n\nCollaborate closely with talented engineers, data scientist and compliance teams across Rain\n\nWork in a fast-paced environment on a rapidly growing product suite\n\nSolve complex problems at the intersection of ML systems, data, and reliability\n\nWhat we're looking for\n5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains\n\nA degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field\n\nProven track record designing and maintaining ML models at scale\n\nAdvanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)\n\nStrong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling\n\nAbility to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems\n\nExperience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques\n\nExperience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale\n\nNice to have, but not mandatory\nDomain expertise in banking, payments, or transaction monitoring\n\nExperience with graph-based or network-level fraud detection techniques\n\nA graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field\n\nExperience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation (in partnership with data science)\n\nKnowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts\n\nThings that enable a fulfilling, healthy, and happy experience at Rain:\nUnlimited time off Unlimited vacation can be daunting, so we require Rainmakers to take 10 days minimum for themselves.\nFlexible 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. New Rainmakers receive a stipend to set up a comfortable home workspace.\nEasy 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.\nRetirement goals Plan for the future with confidence. We offer a 401(k) with a 4% company match.\nEquity plan Every Rainmaker gets an equity option plan so we all benefit from our success.\nHealth 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!\nIn-office meals Rainmakers working from the office enjoy lunch and dinner on us, covered with a DoorDash credit.\nTeam summits Summits play an important role at Rain. Time together helps us build relationships and a common destiny. 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