Member of Technical Staff, Product

Sieve · San Francisco, California · Posted 2026-01-27

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About Us --------

Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data.

We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.

Why Now -------

Sieve is one of the most capital-efficient teams in AI — roughly 30 people serving the world's leading AI labs across every major data modality. You'll join early, own problems end-to-end, and watch your work ship directly into the models defining the frontier.

About the Role -----------------

As a product engineer at Sieve, you'll help build our video collection platform: a system that pays contributors for submitting video recordings alongside internal product tooling.

You'll work full-stack across frontend, backend, systems, and mobile to ship products quickly, improve reliability, and scale the platform in supporting millions of users. You'll also build internal tools that help the team source, review, and deliver high-quality data efficiently. You'll own projects end-to-end.

This role is ideal for someone who enjoys building products with tight feedback loops, thrives in a high-ownership environment, and wants to work directly on the core engine powering frontier video AI.

What You’ll Do -----------------

Requirements ---------------

Bonus --------

Benefits ---------------

About Sieve

Sieve is an ML infrastructure company that provides easy to use APIs, infrastructure, and interfaces for video AI.

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Updated 2026-10-11.