Machine Learning Engineering Manager - Fraud Detection

Role Overview We are seeking a highly experienced, data-driven Fraud Risk Management professional to lead our fraud detection, investigation, and regulatory compliance frameworks. In this role, you will be responsible for safeguarding our digital payments ecosystem (including UPI, IMPS, Wallets, and Merchant platforms) by engineering advanced risk rules, optimizing machine learning models, and leading high-performing operational teams. You will also act as a primary liaison with key regulatory bodies (RBI, NPCI) and Law Enforcement Agencies (LEAs) to ensure ironclad compliance and proactive risk mitigation. Key Responsibilities 1. Regulatory Compliance & Governance  Implement Circulars: Spearhead the end-to-end implementation of all fraud risk-related circulars and compliance mandates from the RBI, NPCI, and FIU.  Regulatory Confirmation: Own and ensure the timely, accurate submission of critical regulatory implementations on the PCOMP portal.  Stakeholder Liaison: Serve as the primary point of contact for ad-hoc risk inquiries from regulatory bodies and Law Enforcement Agencies (LEA).  Executive Reporting: Prepare and present advanced data analysis and high-level risk reports for Board-level forums, including the Risk Management Committee (RMC). 2. Risk Rule Engineering & Advanced Analytics  Rule Development: Codify, test, and deploy hundreds of high-impact risk rules (velocity, blacklists, compliance) using Python and SQL to counter emerging fraud vectors.  Data Analysis: Query and analyze large-scale transactional and profile data to identify patterns, build automated daily alert reports, and perform Root Cause Analysis (RCA).  ML Model Optimization: Partner cross-functionally with Tech and Data Science teams to refine and optimize Machine Learning/AI-driven risk models to boost detection efficiency and reduce false positives. 3. Fraud Investigations & Operations  Investigations: Lead end-to-end investigations into sophisticated digital payme

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. We are the first line of defense against fraud and abuse on the Plaid platform. Our mission is to ensure the safety and integrity of our platform for consumers and customers. As a Fraud and Abuse Operations Analyst , you will be responsible for responding to fraud and abuse events, investigating claims, and triaging incidents. We also partner with product and engineering teams to inform and improve fraud mitigation strategies. Responsibilities: Safeguard Plaid's Platform: Participate in the abuse on-call rotation, directly protecting our users and customers by responding to and resolving fraud and abuse events. Your timely actions will be instrumental in maintaining trust and security. Drive Investigations and Mitigate Risks: Investigate fraud and abuse claims from diverse sources, partnering with senior teammates on complex cases. Your findings will inform decisions and strategies, directly impacting Plaid's ability to prevent future incidents and minimize financial losses. Proactively perform threat modeling of abuse surfaces and continuously survey external fraud trends, adversary techniques, tooling, and emerging threat vectors Support Incident Response: Help triage and manage fraud and abuse ev

At Breeze, we're building the AI-powered infrastructure layer for global commerce, making it radically simpler for businesses to sell, get paid, and operate across markets. We go far beyond traditional payment processing. Breeze combines global payments, AI, stablecoins, and a Merchant of Record-like model to take on the complexity businesses typically manage themselves, including compliance, risk, fraud, chargebacks, reconciliation, and customer support. Our goal is simple: let businesses focus on building and selling great products while Breeze handles the complexity behind getting paid. Backed by Sequoia Capital , Multicoin Capital , and The Chainsmokers , Breeze is a successful, rapidly growing, and exceptionally well-capitalized company. We have the runway to think long term while remaining early enough that every person joining today can have a meaningful impact on what we build. We are hiring a Staff Machine Learning Engineer, Risk! As our Staff Machine Learning Engineer, Risk, you'll lead the evolution of our ML platform for payment risk, building the production-grade capabilities behind feature engineering, model training, deployment, monitoring, and continuous improvement. Risk decisions sit at the center of our business, and you'll own how those models get built, shipped, and kept healthy. This role reports to the CTO. You'll work closely with Risk, Software Engineering, and Data Engineering, and you'll be the senior technical voice for ML on the risk team. We're looking for someone who thrives in fast-moving environments, wants meaningful ownership, and is excited to build rather than simply maintain. What You'll Do Design and build ML infrastructure for payment risk detection, using Databricks as the core platform, in close partnership with software and data engineers. Bring structure to the team's ML environment: feature pipelines, versioning, job orchestration, and monitoring. Design and productionize models rather than just prototype them, including

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community. You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community. The Difference You Will Make: As a Senior Machine Learning Engineer on the Trust team, you will actively contribute code and ideas that shape the ML systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end — from designing and training models to productionizing and operating them at scale, while collaborating closely with cross-functional partners. You'll tackle real-world challenges such as account takeover, fake accounts, payment fraud, and bot detection. Your work

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Seattle, Washington D.C., Raleigh, London, and Amsterdam. Fraud Data is the data science and machine learning team within Plaid’s Fraud organization, responsible for using data and ML to improve and scale Plaid’s fraud products. Within Fraud Data, the Customer & Product Intelligence team focuses on understanding product performance, uncovering customer insights, and enabling go-to-market teams with data-driven solutions. The team partners closely with customers and GTM teams on fraud analyses and proofs of concept, turning customer learnings into scalable, reusable product capabilities. We also build the metrics, analytics, and data foundations that measure product health, identify opportunities for improvement, and guide product decisions across Plaid’s Fraud portfolio. As a Data Science Manager, you will lead a team responsible for customer-facing data science and Fraud product analytics. You will set the team's roadmap, develop its data scientists, and remain involved in analytical methods, technical reviews, and customer investigations. You will: Set a 6–12-month roadmap with Product, Engineering, and GTM, and assign priorities and responsibilities across the team. Define product metrics, their underlying data, and reporting and

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Imagine getting off a long international flight, standing in the freezing rain, attempting to contact your Airbnb host, only to realize that they are not responding because the Airbnb that you booked was not real. The Listing Integrity team’s ultimate goal is to prevent experiences like this by proactively detecting and removing fraudulent listings (Homes, Experiences, and Services) so that guests can search and book on Airbnb with confidence. The team uses data driven heuristics, machine learning models, and customer service operations in order to accomplish this goal. The Difference You Will Make: As a Software Engineer on the Listing Integrity team, you will be working with data scientists, designers, product managers, and customer service operations to innovate new ways we can stop bad actors in the ever evolving fraudulent listing creation and financial losses associated with it by collaborating across team boundaries. On this team, you must have the curiosity to dig deep into various end to end systems in order to understand how and where the fraud occurs. Your curiosity will be rewarded with finding projects that have outsized impacts on decreasing fraud losses and protecting Airbnb users from bad experiences while on vacation. A Typical Day: Work with large scale back end systems to detect fraudsters, using rules and connecting to productionalized machine learning models. Work collaboratively with cross-functional partners including machine learning engineers, product managers, operations and data scientists, identify opportunities for business impact, und