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our company's ML Platform group builds and operates the core infrastructure that powers large-scale AI workloads across on-prem bare-metal GPU clusters and multi-cloud environments. Our goal is to deliver the modern infrastructure and tooling that accelerates our company's entire algorithm development lifecycle – from a researcher's first experiment to a production deployment.
We are a small, independent group of engineers with diverse skills across software, infrastructure, and systems. We set the standards, build the cross-company products, and take end-to-end ownership of everything we ship.
What will your job look like?
Design, develop, and maintain the Python framework that enables algorithm developers across our company to train, validate, quantize, and deploy deep learning models – locally, on-prem, and across cloud providers – through a single unified interface
Build high-performance data streaming libraries that feed large-scale distributed training pipelines in Rust with Python interfaces
Set the standard for reliable, reproducible research at scale – experiment tracking, configuration management, checkpoint handling, and multi-node training
Work directly alongside algorithm researchers to understand friction, propose solutions, and ship them – without layers of process in between
Contribute to open source when the right fix/feature belongs upstream.