About the role
We are looking for an ML Infrastructure Engineer with 3+ years of experience to own and scale the training and inference stack at a fast-growing AI document processing platform. You'll be a strong generalist who understands the mechanics of how ML models work – from serving and monitoring to building robust data pipelines – and can improve inference performance, reliability, and cost efficiency. This is a high-impact IC role where you'll work closely with ML researchers to ensure models are deployed quickly and reliably, and that infrastructure is never a bottleneck for the products being served. The ideal candidate is AI-native from the get-go, comfortable with 1-to-3 node training and single-to-double node serving, and thrives in a fast-paced startup environment.
What you will be doing
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Building and maintaining model serving infrastructure – improving inference speed, monitoring, and reliability to ensure it's never a bottleneck for customers
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Setting up and improving training infrastructure for models ranging from 300M to 30B parameters across 1-to-3 node environments
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Developing observability, logging, and monitoring systems across the ML stack
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Building internal data pipelines and tooling to help ML researchers move faster from experiment to production
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Architecting infrastructure to arbitrate inference between multiple cloud providers while optimizing for accuracy, latency, and cost
