Senior Machine Learning Engineer/Research Scientist

About the Company

Tools for Humanity (TFH) designs and builds technology behind World. World is building a real human network designed to accelerate people in the age of AI. The TFH and World tech stacks make this possible through the Orb, World ID, and World App, adding a human layer to an AI-driven internet.

Responsibilities

• Improve core biometric identification and anti-spoofing models, iterating on deep learning architectures and data pipelines under strict latency and memory constraints.
• Utilize classical computer vision and image processing for identification, detection, or quality-assessment problems.
• Lead independent research initiatives from hypothesis formation to experimental design and results analysis.
• Analyze misclassified samples to drive model iterations and form concrete hypotheses.
• Build evaluation and monitoring pipelines to catch model regressions and data drift.
• Take work from prototype through rigorous testing to deployed systems, partnering with Mobile and Orb software teams.
• Write design docs, experiment write-ups, and technical proposals to drive alignment.
• Shape technical standards and mentor junior researchers and engineers.

Requirements

• Strong fundamentals in classical computer vision and image processing (OpenCV, NumPy).
• Deep experience training and shipping production-quality deep learning models for computer vision.
• Experience working under tight latency and memory constraints.
• Pragmatic, applied-research mindset with strong experimental discipline.
• Solid mathematical fluency.
• Collaborative operating style with a focus on mentorship and knowledge sharing.

Preferred Qualifications

• Direct experience with biometric identification at scale.
• Knowledge of margin-based metric learning losses (ArcFace, Triplet).
• Experience with anti-spoofing or presentation attack detection.
• Experience with red-teaming or adversarial evaluation of ML systems.
• Publications at top ML venues.
• Hands-on experience with Rust for high-performance code.
• Experience with edge optimization (quantization, pruning, distillation) and on-device deployment.
• Background in sensors, imaging, computational photography, or camera ISPs.