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Senior Site Reliability Engineer

Hive · On-site

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Last seen by MeritLog September 10, 2026Source: LeverSource version: lever-postings-v1

Source: the employer's Lever job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model
On-site
Salary
Not listed by source
Location
Seattle
Occupation
Software Developers(O*NET 15-1252.00)

Job description

About Hive Hive is the leading provider of cloud-based AI solutions to understand, search, and generate content, and is trusted by hundreds of the world's largest and most innovative organizations. The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving billions of customer API requests every month. Hive also offers turnkey software applications powered by proprietary AI models and datasets, enabling breakthrough use cases across industries. Together, Hive’s solutions are transforming content moderation, brand protection, sponsorship measurement, context-based ad targeting, and more. Hive has raised over $120M in capital from leading investors, including General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. We have over 250 employees globally in our San Francisco, Seattle, and Delhi offices. Please reach out if you are interested in joining the future of AI! DevOps and Systems Team Our unique machine learning needs led us to open our own data centers, with an emphasis on distributed high performance computing integrating GPUs. Even with these data centers, we maintain a hybrid infrastructure with public clouds when the right fit. As we continue to commercialize our machine learning models, we also need to grow our DevOps and Site Reliability team to maintain the reliability of our enterprise SaaS offering for our customers. Our ideal candidate is someone who is able to thrive in an unstructured environment and takes automation seriously. You believe there is no task that can’t be automated and no server scale too large. You take pride in optimizing performance at scale in every part of the stack and never manually performing the same task twice.

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