Machine Learning, Platform Engineer

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About this role

Machine Learning, Platform Engineer San Francisco About the Role Our team focuses on enabling custom models and dedicated inference on Together. We are responsible for building a container platform, optimizing autoscaling, minimizing cold starts, achieving the best end-to-end model performance, and providing a best-in-class developer experience with great tooling. We often focus on video or audio generation across the stack: CUDA kernels, pytorch optimization, inference engines, container orchestration, queueing theory, etc. An ideal candidate will be great at profiling/optimization but know the word kubernetes, or be intimately familiar with multi-cluster scheduling and have some sense of ML bottlenecks. Responsibilities - New hires may work on multi-cluster orchestration, portfolio optimization, predictive autoscaling, control panes, model bring-up, model optimization, APIs for managing deployments, inference worker SDKs, and CLI tools. - Analyze and improve the robustness and scalability of existing distributed systems, APIs, databases, and infrastructure - Partner with product teams to understand functional requirements and deliver solutions that meet business needs - Write clear, well-tested, and maintainable software and IaC for both new and existing systems - Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance Requirements - 5+ years of demonstrated experience in building large scale, fault tolerant, distributed systems. - Experience running serverless inference platforms, doing model bring-up on short notice, being on call, or running a cloud provider is a very big plus - Good taste and ability to thoughtfully discuss how what you've built has failed over time - Experience

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