About The Team

The AI & Biometrics team at Tools for Humanity owns the machine learning systems behind the World Network. Our iris and face recognition systems, our anti-spoofing pipeline, and the models running on the Orb and on phones are what make Proof of Personhood actually work at scale.

Within the Face team, we develop systems for face verification, uniqueness and duplicate detection, presentation attack detection, and supporting capabilities such as face detection, image quality assessment, occlusion detection, pose estimation, and others that allow these systems to operate reliably in real-world conditions.

Our work spans the full machine learning lifecycle: defining data-collection and labeling requirements, curating datasets, conducting applied computer-vision research, training and evaluating models, adversarial testing, supporting deployment, investigating production behavior, and monitoring performance after release.

The problems we work on are grounded in real-world conditions. Our systems must handle variation in cameras, lighting, pose, image quality, occlusion, user behavior, and attack methods. They must also operate within strict latency and memory budgets and interact with privacy-preserving systems (Anonymized Multi-Party Computation) that compare users against a growing identity set. At our scale, small regressions can have a meaningful impact, so evaluation, operating-point selection, and production monitoring are central to how we work.

We are pragmatic about our methods. We use deep learning where it provides clear value, classical computer vision and image processing where they are more efficient or reliable, and hybrid approaches when they produce the best system.

The team is well supported. We have a dedicated Mobile subteam that owns deployment to mobile devices, ML Infrastructure and MLOps subteams that own the data pipelines, training clusters, GPU fleet, and much of the underlying tooling, and Face and Iris subteams dedicated to each modality. Face engineers own problem formulation, data requirements, data-quality decisions, model behavior, evaluation methodology, and investigation of model failures for their projects to succeed. We also work closely with the Orb software, Mobile, Proof of Personhood, and Product teams to take ideas to production.

In This Role, You Will

• Own face machine learning projects from initial problem definition through data preparation, experimentation, evaluation, production validation, and monitoring, while partnering with different teams.
• Improve our core biometric identification and anti-spoofing models, training and iterating on deep learning architectures, losses, and data pipelines, with model size, latency, and memory budgets as first-class design constraints from day one.
• Use classical computer vision and image processing when they are the right solution, whether as a complete method, a preprocessing stage, a lightweight on-device component, or a diagnostic tool. We are not a deep learning only team, and on-device compute rewards the discipline of using the lighter solution when it fits.
• Lead independent applied ML initiatives end-to-end: form a written hypothesis, design ablations that isolate variables, run experiments, read results honestly, and know when to ship and when to stop chasing the last percentage point
• Work directly with face images and datasets: improve collection and labels, inspect difficult samples, identify failure modes, and use those findings to drive model and evaluation changes.
• Build evaluation pipelines that catch model regressions before they reach production, monitoring that can reveal data drift, score-distribution changes, new attack patterns, cohort-specific regressions, or unexpected model behavior, and improve internal tools for data analysis, model training, evaluation, visualization, red teaming, and monitoring.
• Read and evaluate relevant computer vision and biometrics research, adapting useful ideas to our systems without treating paper reproduction as the objective.
• Write design documents, experiment reports, post-launch analysis and technical proposals that remain useful after the project is complete, and others can understand and build on your work.
• Help shape technical standards across the AI & Biometrics team: evaluation methodology, experimentation discipline, model versioning, monitoring, and across any engineering and scientific standards of the team.