Role overview
Building an identification engine on Worldcoin’s scale requires a deep understanding of our data. Through dedicated field tests we receive data that is forwarded into our knowledge graph. This graph is not only used to generate datasets for downstream consumption in ML models, but can also be leveraged to detect fraud and much more. This role is responsible for developing the knowledge graph, generating insights, and creating large high-quality datasets to train various ML models.
What you'll work on
- Apply various machine learning algorithms to raw image data in order to create and validate biometric datasets. This might include: face recognition using neural networks, traditional iris recognition using Gabor wavelets, etc.
- Analyze patterns in metadata to detect inconsistencies and find fraud cases.
- Implement new field tests with our distribution and data collection teams to create larger datasets.
- Build and refine custom data labeling services that directly influence the quality of our iris recognition engine.
- Work closely with other stakeholders (data contributors + consumers) to incorporate their data usage needs on a variety of tasks and domains.
Tags & focus areas
Used for matching and alerts on DevFound Data Science Scientist Remote Tensorflow Pytorch Python Aws