Machine Learning Engineer - Fraud Risk

About the Company 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 stablecoins 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. 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. 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. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the Team 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. What you’ll do 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 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 at least 10 days off. Flexible working ☕ We support a flexible workplace. If you feel comfortable at home, please work from home. If you’d like to work with others in an office, feel free to come in. We want everyone to be able to work in the environment in which they are their most confident and productive selves. New Rainmakers will receive a stipend to create a comfortable home environment. Easy to access benefits 🧠For US Rainmakers, we offer comprehensive health, dental, and vision plans for you and your dependents, as well as 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 📦 We offer every Rainmaker an equity option plan so we can all benefit from our success. Rain Cards 🌧️ We want Rainmakers to be knowledgeable about our core products and services. To support this mission, we issue a card for our team to use for testing. Health and Wellness 📚 High performance begins from within. Rainmakers are welcome to use their card for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! Team summits ✨ Summits play an important role at Rain! Time spent together helps us get to know each other, strengthen our relationships, and build a common destiny. Expect team and company off-sites both domestically and internationally.

{
  "content_hash": "442358a00f72e5d1b5c433da712bb02219b0ee2765e373fb859bf1ab6a73b6fe",
  "source_hash": "43bd5a4712d978c87023fc5cb752e4abc411c57a905e227c7e9937daf36b1393",
  "last_changed_at": "2026-05-29T06:41:36.703Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "New York, NY",
    "city": "New York",
    "region": "NY",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.9
  },
  "salary_max": null,
  "salary_min": null,
  "inferred_at": "2026-06-06T09:27:49.550Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "New York, NY",
      "city": "New York",
      "region": "NY",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.9
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "hybrid",
  "salary_period": null,
  "workplace_type": "hybrid",
  "salary_currency": null
}

{}

{
  "id": "21bbb841-fe8c-41e3-abde-da66086e08f0",
  "team": "Engineering",
  "title": "Machine Learning Engineer - Fraud Risk",
  "jobUrl": "https://jobs.ashbyhq.com/rain/21bbb841-fe8c-41e3-abde-da66086e08f0",
  "address": null,
  "applyUrl": "https://jobs.ashbyhq.com/rain/21bbb841-fe8c-41e3-abde-da66086e08f0/application",
  "isListed": true,
  "isRemote": false,
  "location": "New York, NY",
  "updatedAt": null,
  "apiVersion": "ashby-non-user-graphql-v1",
  "department": "Engineering",
  "publishedAt": null,
  "workplaceType": "Hybrid",
  "employmentType": "FullTime",
  "secondaryLocations": []
}