Mlops Engineer

Drive core components of our MLOps technical roadmap concerning high-throughput online inference, active learning for supervised approaches, and reproducible ML pipelines at scale - Design and implement reference architectures for large-scale ML systems to support both training and serving workloads - Collaborate and knowledge-share with our Core Infrastructure team to compress time-to-production for Machine Learning - Build tooling and pipelining abstractions to allow ML engineers to focus on experimentation while empowering self-service todeploy and serve their models reliably and consistently - Help ML engineers produce clean, reproducible, and highly performant machine learning through rigorous code review with a lens on software quality - Advocate for automation and monitoring at all steps of ML system construction and help to define best practices based on personal industry experience and research - Participate in sprint planning, estimations, and reviews You Have: