Quick overview
SeniorityMid Senior
Employment typeFull Time
Work modeOn Site
LocationSan Francisco, United States
Machine Learning
Job description
About Deploy
Deploy is an intelligence infrastructure platform that gives developers and enterprises access to open source and proprietary AI models. For leading open source LLMs, Deploy operates an optimized model inference engine that keeps costs and latency low while maintaining high throughout. Deploy also offers a set of proprietary AI models that address challenges in content safety, AI & deepfake detection, sponsorship intelligence, and IP & publicity protection. Trusted by the world's largest and most innovative organizations, Deploy's solutions unlock high-performance AI model inference at production scale.
Deploy has raised more than $200M from leading investors, including Tomales Bay Capital, General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi.
Deploy has raised more than $200M from leading investors, including Tomales Bay Capital, General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi.
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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