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Talent Acquisition Specialist

SieveSan Francisco
On-site Full-time

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Experience Level

Experience

Qualifications

RequirementsMinimum of 3 years of experience in technical recruiting. Strong understanding of the AI/ML/research talent landscape. Must be available for in-person work at our San Francisco headquarters. Preferred: Experience as a founding recruiter. Preferred: Proven track record in hosting recruiting events. Preferred: Familiarity with AI tools (e.g., Claude, Cursor).

About the job

About Us

Sieve is at the forefront of AI innovation, specifically focusing on video data. Our advanced infrastructure harnesses exabyte-scale video resources and cutting-edge techniques to create unparalleled datasets that revolutionize video modeling. As video content accounts for 80% of internet traffic, it is the backbone of creativity, communication, gaming, AR/VR, and robotics. Our mission is to tackle the critical challenge of providing high-quality training data, essential for the growth of these applications.

In a short span, our team of just 15 has partnered with leading AI labs and generated $XXM last quarter. We proudly secured Series A funding from top-tier firms like Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.

About the Role

As our inaugural recruiter at Sieve, you will be instrumental in building our recruiting function from scratch, focusing on engineering and product operations. This role demands a high level of ownership and a proactive approach as you navigate a fast-paced environment, ensuring we attract top talent to scale our high-growth startup.

In this dynamic role, you will engage in various activities including sourcing candidates, conducting screening calls, organizing recruiting events, and any other tasks essential to creating a talent-rich organization.

About Sieve

Sieve is a pioneering AI research lab dedicated to video data, developing innovative datasets that advance the field of video modeling. With strategic partnerships and substantial funding, we are committed to solving the challenges of high-quality training data for various digital applications.

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