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About the Team: The AI & Biometrics team is building a state-of-the-art iris recognition engine that works on the 1B+...

Dev Machine Learning Infrastructure Kubernetes

$98k - $156k* · Senior Fully remote

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About the Team: The AI & Biometrics team is building a biometric iris recognition system that can work reliably with ...

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About the AI & Biometrics Team: The AI & Biometrics team is building a biometric iris recognition system that can wor...

Dev Machine Learning Remote Engineer

$98k - $156k* · Senior Fully remote

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Selected role

Machine Learning Infrastructure Engineer

Worldcoin.org remote

$98k - $156k Senior Full-time

About the Team:

The AI & Biometrics team is building a state-of-the-art iris recognition engine that works on the 1B+ people scale. In order to do this, we use a fusion of custom optics, hardware, and on-device machine learning, combined with large-scale data collection in more than 20 countries to amass over several million images monthly. These images need to be pre-processed and passed through both external and in-house labelling services.

About the Opportunity:

From field tests all over the world we receive data from various demographics to train our ML models. These images need to be pre-processed and passed through our labelling services before they can be used for training neural networks. This role is responsible for building, scaling, and maintaining a stable data pipeline.

In this role you will:

Design data pipelines to handle large scale data ingest. This includes figuring out ways to store and process this data with robust features for filtering, pre-processing, and versioning.
Build out data infrastructure to train large neural networks using self-supervised and contrastive learning.
Build and refine custom data labeling services that directly influence the quality of our iris recognition engine.
Work closely with other internal stakeholders to incorporate their data usage needs.

About You:

Have industry experience as a Data Engineer, Machine Learning Engineer, or Data Scientist, dealing with data infrastructure, distributed systems, and fault tolerant data pipelines.
Experience deploying models and infrastructure on Kubernetes.
Experience with infrastructure tools for provisioning, deployment, and monitoring such as Terraform, AWS, Docker, and Datadog.
Experience with heterogeneous data sources and data models including MongoDB, PostgreSQL, Redis, and Neo4J.
Own problems end-to-end, and are willing to pick up whatever context is needed.
You enjoy working as part of a fast-moving team, where perfectionism can sometimes be at odds with pragmatism.
A desire to dig into problems across the stack, whether networking issues, performance bottlenecks, memory leaks, or simply reading unfamiliar code to figure out where potential issues might exist.
Have a strong belief in the crucial need of high-quality data for producing state of the art machine learning systems.

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