Senior Machine Learning Engineer/ Research Scientist

The team is well-equipped to take work end-to-end. We have a dedicated Mobile subteam that owns deployment to mobile devices, and ML Infrastructure and MLOps subteams that own the data pipelines, training clusters, GPU fleet, and the tooling underneath everything we ship. That means researchers and engineers on

the team can focus on the modeling work itself, with the heavy lifting around data, compute, and deployment handled in-house rather than outsourced or improvised. We also work closely with the Orb software, Proof of Personhood, and Product teams to take ideas all the way from research notebook to deployed binary.

About

the role We are looking for a Senior or Staff Machine Learning Engineer / Research Scientist to help push our biometric systems past the next order of magnitude in performance and security. This is a role for someone with deep experience and strong technical judgment, who can operate independently on hard, open-ended problems and raise the bar across

the team. You will be a major contributor to the core identification and anti-spoofing stack, ship measurable improvements against our production thresholds under tight on-device SLA and memory constraints, and lead independent research initiatives on open problems in identification and presentation attack detection. Over time, you will help shape evaluation methodology, experimentation discipline, and model monitoring across

the team, mentor more junior researchers and engineers, and become a recognized authority on the hardest problems at the intersection of biometric ML, secure computation, and global-scale identity. We strongly believe in being in the driver's seat - owning your work end-to-end, identifying the right problem, running the experiments, shipping the result, and standing behind it once it is in production. This is a senior IC role; you will operate with significant independence, but the work only ships when it survives contact with the teams downstream of yours, such as Mobile, Orb software, and Proof of Personhood, whose constraints (latency budgets, memory envelope, MPC compatibility, security review) define what "done" actually looks like. In this role, you will: 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. Reach for classical computer vision and image processing wherever it is the right tool, whether as the actual solution to an identification, detection, or quality-assessment problem, as the preprocessing stage of a deep learning pipeline, or as diagnostic tooling for understanding what a model is seeing. 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 research 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. Look at the data when models fail. Pull up misclassified samples, form concrete hypotheses about why they failed, and use that to drive the next iteration, rather than waiting for an aggregate metric to explain the problem for you. Build evaluation and monitoring pipelines that catch model regressions before they reach production and surface data drift in the wild, including across the AMPC database, not weeks later in a post-mortem. Take work all the way from prototype through rigorous testing to deployed system, partnering with different teams. Translate between ML, embedded, and secure-compute constraints when the conversation needs it. Write design docs, experiment write-ups, and technical proposals that hold up to scrutiny across teams and remain useful months after they were written. Drive alignment on contentious decisions. Help shape technical standards across the AI & Biometrics team - evaluation methodology, experimentation discipline, model versioning, monitoring, and mentor more junior researchers and engineers as a default behavior, not as an extra task. You might thrive in this role if you have: An " in-the-driver's-seat " operating style: you take ownership of problems end-to-end, drive your work forward without waiting for direction, and stand behind your decisions once they are in production. Strong fundamentals in classical computer vision and image processing - OpenCV, NumPy, the standard toolkit of filters, transforms, morphology, geometric methods Deep, hands-on experience training and shipping deep learning models for computer vision at production quality, including under tight latency and memory constraints. Exposure to real edge or embedded deployment is a strong plus; A pragmatic, applied-research mindset : you care about rigor and depth, but you know when a result is good enough to ship and when chasing the last percentage point on a benchmark is the wrong use of your time. Solid mathematical fluency at the level where you can spot pathologies in proposed designs without running them. Experimental discipline : hypotheses written down before code is, ablations that isolate one variable at a time, and the judgment to know when a result is real and when it needs more seeds. A collaborative operating style : you mentor, share knowledge by default, and engage constructively with constraints from neighboring teams rather than treating them as obstacles. We are not looking for a lone wolf, however brilliant. Strong plus : direct experience with biometric identification at scale; margin-based metric learning losses (ArcFace, Triplet and their variants) and their failure modes; anti-spoofing / presentation attack detection; red-team or adversarial evaluation of ML systems; publications at top ML venues. Additional Nice-to-haves: Hands-on experience with Rust for high-performance code paths, and the disposition to optimize for speed rather than treat it as someone else's problem. Experience with edge optimization and on-device deployment of ML models. Quantization, pruning, distillation, kernel-level optimization, deployment to mobile NPUs, embedded GPUs, microcontrollers, or other constrained targets. A background in sensors, imaging, computational photography, or camera ISPs.

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