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Associate Product Manager, Deploy Models

Hive

Hive is a leading provider of AI solutions to understand, search, and generate content, trusted by large organizations for content moderation, deepfake detection, and digital safety.

Hive is the employer — EngRadar is a job radar, not a recruiter. We track this posting from their own careers page and send you straight there; we never handle applications or CVs.

San Francisco Full-time Posted 29d ago aicontent-moderationdeepfake-detection

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.

Associate Product Manager, Deploy Models Role

As an Associate Product Manager on our Deploy Models team, you will work cross-functionally with stakeholders to help define product requirements and support implementation efforts, collaborating closely with our Machine Learning, Core Infrastructure, and Product teams. You will play an important role in the core of our business: our cloud-hosted deep learning models. You will help support Deploy's growth by contributing to our innovative capabilities and building a strong foundation for future ownership within the product org.

Posted by Hive on their own careers page — you apply directly, no recruiter in between. View original / apply →

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