Responsibilities
- Lead end-to-end development and maintenance of the identity verification machine learning stack, covering document validation, facial matching, liveness checks, and defenses against presentation attacks and deepfake intrusions.
- Design and implement graph neural network-based identity graph systems to link accounts by shared biometric, device, and document traits, identifying synthetic identity networks and organized fraud during sign-up.
- Create real-time behavioral and device intelligence models to flag suspicious capture sessions, distinguish automated bots from genuine users, and assess risk using device fingerprints.
- Evaluate third-party machine learning providers using proprietary test sets, establish adaptive routing across regions and vendors, and build internal monitoring to prevent model regressions from impacting users.
- Guide and support experienced and mid-tier engineers while collaborating with platform and risk-focused ML teams to standardize system design across the organization.
Work Arrangement
Hybrid
Other
- Operates as a remote-first organization with periodic in-person collaboration events known as 'surges' held each quarter.
- Applies generative AI tools with careful governance and human review to maintain ethical standards.
- Required to interpret financial compliance regulations and evolving fraud patterns into actionable machine learning strategies.
