Machine Learning Infrastructure Engineer

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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