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 powered by proprietary AI models and datasets, unlocking breakthrough applications for critical business needs with deep learning and generative AI. Collectively, Hive's technology is transforming approaches to platform integrity / content moderation (including AI-generated content detection), brand protection, sponsorship measurement, context-based ad targeting, and more.
Hive has raised over $120M from leading investors, including General Catalyst, 8VC, Tomales Bay Capital, and Glynn Capital. In April 2021, Hive announced a $50M Series D at a $2B valuation. The San Francisco-based company has 200+ full-time employees globally, in addition to a distributed workforce of more than 5 million global contributors that supports data labeling operations.
For more information, visit thehive.ai or follow on LinkedIn.
You will support the product roadmap and development lifecycle for AI model inference engines by collaborating with engineering and infrastructure teams.
Additionally, you will help define functional requirements and track project progress to ensure successful product delivery.
What you need
Candidates must hold a Bachelor of Science in Computer Science, Engineering, or a related field with 0-2 years of experience.
You should have hands-on exposure to machine learning frameworks and proficiency in Python or Node.js.
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.
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Responsibilities
Support the roadmap, functional requirements, and development timeline for our Deploy Models, under the guidance of senior PM/leadership
Develop an understanding of our customers' needs across various industries
Assist in product execution from inception to delivery
Help engineering and product teams break down projects into manageable parts, and help track progress against delivery plans
Support our Deploy Models portfolio by learning the needs of the product development teams, and contributing to the creation of our deep learning models that provide human-like interpretation of video, image, audio, and text
Help communicate product benefits to our users and internal stakeholders
Work with senior product and engineering stakeholders to help define new features and improve models habitually
Help track engineering velocity and support the identification of best practices to improve delivery speed and security
Maintain awareness of industry best practices for data maintenance handling as it relates to your role
Adhere to policies, guidelines, and procedures pertaining to the protection of information assets
Report actual or suspected security and/or policy violations/breaches to an appropriate authority
Requirements
You have a Bachelor of Science in Computer Science, Engineering, or similar field
You have some hands-on exposure to modern machine learning libraries, frameworks, and technologies (coursework, internships, or professional experience)
You have excellent verbal and written communication and presentation skills
You have a working knowledge of functional programming languages such as Python or Node.JS
You have 0-2 years of combined Machine Learning and Product Management experience
You are comfortable with — and can thrive in — an environment that is constantly changing, frequently uncertain, and at times chaotic
You are detail-oriented, organized, collaborative, and hands-on
You are eager to grow into a leadership role, with an interest in eventually owning team goals and deadlines
Beyond just shipping new products, you obsess about continuous product improvement
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Who We Are
We are a group of ambitious individuals who are passionate about creating a revolutionary AI company. At Deploy, you will have a steep learning curve and an opportunity to contribute to one of the fastest growing AI start-ups in San Francisco. The work you do here will have a noticeable and direct impact on the development of the company.
Thank you for your interest in Deploy and we hope to meet you soon!
The current expected base salary for this position ranges from $90,000 - $120,000. Actual compensation may vary depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the total compensation package that is provided to compensate and recognize employees for their work; stock options may be offered in addition to the range provided here.
