Senior Machine Learning Engineer
Tools for Humanity is a technology company building products for humans in the age of AI.
Maintainer signals as of 9/25/2026
About Tools for Humanity
Tools for Humanity develops hardware, software, and services connected to World, described as an open-source real-human network. Its products include the Orb for privacy-focused proof-of-human verification, World App for World ID access, digital asset management, wallet functionality, transactions, and Mini Apps, and World Spaces for verification, community programming, and events. The company serves end users and organizations seeking to integrate World infrastructure into their projects.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Own face machine learning projects from problem definition through production monitoring, improving biometric identification and anti-spoofing models, evaluation pipelines, and engineering standards.
Requirements
- Hands-on experience training, evaluating, and shipping deep learning systems for computer vision
- Understanding of latency and memory constraints
- Ability to structure ambiguous machine learning problems into technical plans
- Knowledge of data pipelines, augmentations, architectures, loss functions, optimization, tuning, and failure analysis
- Foundations in classical computer vision and image processing
- Experience with OpenCV, NumPy, or equivalent libraries
- Fluency in Python and a modern deep-learning framework such as PyTorch
- Experience designing production evaluations, metrics, thresholds, calibration, datasets, slicing, leakage prevention, and regression analysis
- Ability to write maintainable research and production-quality code
- Strong written communication and collaborative working style
Responsibilities
- Own face machine learning projects end-to-end
- Improve biometric identification and anti-spoofing models
- Apply classical computer vision and image processing
- Lead applied machine learning initiatives
- Improve face-image collection and labeling
- Build evaluation pipelines and production monitoring
- Evaluate computer vision and biometrics research
- Write technical documentation and proposals
- Shape standards for evaluation, experimentation, model versioning, and monitoring