Senior Machine Learning Engineer
Tools for Humanity is a technology company building products for humans in the age of AI. It contributes to World and develops proof-of-human hardware and software, including the Orb, alongside World App services for World ID and digital-asset management.
Maintainer signals as of 8/14/2026
Projects
About Tools for Humanity
Tools for Humanity is a technology company founded in 2019 and headquartered in San Francisco and Munich. It builds and operates services including World ID App and World App, which provide interfaces for creating and using World ID, accessing Mini Apps, and managing digital assets through a self-custodial wallet. It also develops the Orb and Orb Mini proof-of-human hardware and software, using encrypted iris-imaging processes and open-source components, and leads design and planning for World Spaces.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You own face machine learning projects from problem definition through data preparation, experimentation, evaluation, production validation, and monitoring. You improve biometric identification and anti-spoofing models, use classical computer vision and image processing, build evaluation and monitoring pipelines, investigate model failures, evaluate research, document technical work, and help establish engineering and scientific standards.
Requirements
- Hands-on experience training, evaluating, and shipping deep learning systems for computer vision
- Practical understanding of latency and memory constraints
- Experience structuring ambiguous machine learning problems into technical plans
- Knowledge of data pipelines, augmentations, architecture selection, loss functions, optimization, hyperparameter 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
- Use classical computer vision and image processing where appropriate
- Lead applied machine learning initiatives
- Improve face-image collection and labeling
- Inspect difficult samples and identify failure modes
- Build evaluation pipelines and production monitoring
- Improve tools for data analysis, training, evaluation, visualization, red teaming, and monitoring
- Evaluate computer vision and biometrics research
- Write design documents, experiment reports, post-launch analyses, and technical proposals
- Shape technical standards for evaluation, experimentation, model versioning, and monitoring