Senior Staff Software Engineer (Identity & Risk Intelligence)

Join GoFundMe as a Senior Staff Software Engineer, focusing on Identity & Risk Intelligence. You will set the technical direction for how GoFundMe recognizes, resolves, and trusts users on its platform. This role involves building a progressive identity layer, architecting identity resolution, and shaping login risk into adaptive experiences. You will also own the identity and risk intelligence technical roadmap and partner with various teams across the company.

• Set the technical direction for how GoFundMe recognizes, resolves, and trusts the people on its platform, and lead the engineering behind it.
• Build a progressive identity layer that defines how GoFundMe recognizes returning visitors, stitches anonymous device history to a person at signup or login, and raises identity confidence over time.
• Architect identity resolution and the identity graph: visitor stitching, account linking, and confidence-weighted models that power personalization, guest donor checkout prefill, and fraud prevention across our consumer and enterprise surfaces.
• Strong systems thinking about identity as a risk problem: you understand the difference between authenticating a user and being confident in their identity over time, and you are pragmatic about the friction-vs-security tradeoff and unit economics at scale
• 8+ years of software engineering experience, with significant time at senior, staff, or principal levels working on identity, risk, fraud, or trust and safety platforms
• Fluency with login risk and adaptive authentication, including risk scoring, step-up flows, and device signals, applied to user experience rather than detection alone
• Track record of designing and shipping identity resolution, identity graph, behavioral signal, or risk scoring systems that other teams depend on in production
• Deep experience with the technical patterns that underlie this domain: device fingerprinting, visitor stitching, account linking, behavioral feature engineering, confidence calibration, and risk model integration
• Deep identity and CIAM platform experience at scale: auth, sessions, passwordless authentication, social login, and the engineering decisions that balance security against conversion
• Background at a fintech, payments, marketplace, social, or trust and safety-forward company where identity, fraud, and risk were treated as a unified platform problem
• CIAM platform experience (Descope, Okta, Auth0, or comparable) including risk-based and adaptive auth models
• Familiarity with device fingerprint and risk vendors (Alloy, Fingerprint.js, ThreatMetrix) as input signals to a risk and identity confidence layer
• Experience with behavioral analytics and ML-adjacent systems, including feature stores, signal pipelines, and model serving infrastructure, even if you are not primarily an ML engineer
• Experience with compliance and regulatory framing around identity signals (KYC, BSA/AML adjacent, GDPR, CCPA), particularly where device or behavioral signals are involved as approved identifiers
• Public contributions, talks, or thought leadership in the identity, risk, fraud, or trust and safety space