What the role involves
About Us
Sieve is the only AI research lab exclusively focused on video data. We combine exabyte-scale video infrastructure, novel video understanding techniques, and dozens of data sources to develop datasets that push the frontier of video modeling. Video makes up 80% of internet traffic and has become the enabling digital medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in growth of these applications: high-quality training data.
We've partnered with top AI labs and did $XXM last quarter alone, as a team of just 15 people. We also raised our Series A last year from Tier 1 firms such as Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
About the Role
As a software engineer at Sieve, you’ll work across the stack to build and scale the data pipelines that create the datasets we deliver to customers. You’ll have ownership over projects end-to-end: from how data is sourced and curated, to developing ML filters, improving system efficiency, and building internal dashboards for QA and delivery. You’ll play a critical role in ensuring customers receive high-quality data on time, every time.
This role is ideal for someone who thrives on solving hard problems, enjoys working directly with customers pushing the frontier of Video AI, and wants to be challenged to achieve at the highest level.
Requirements
Strong Python developer, experience in Go or Typescript also welcome
Excellent communication skills, especially with customers and external teams
Motivated by hard problems that require working late nights and weekends
Writes clean, maintainable code
Able to context switch at a moment's notice
Bonus
Experience as an early hire at a startup
Bonus
Active GitHub or portfolio projects
In-person at our SF HQ
Benefits
401k + Full Health Insurance
Breakfast, Lunch, and Dinner covered and your choice of snacks
Ubers covered home
About sieve
sieve solves data cleaning for hedge funds and investment firms by letting them get clean data in four lines of code. Currently, their data pipelines have conditions that raise for human review, which literally send an email to engineers with data that needs to be reviewed. We provide an API that integrates directly into their existing pipeline - instead of raising for human review, they can send all the same information to our API and get clean, high-quality data back. By using our AI agents built specifically for financial data collection, along with expert-in-the-loop review, we provide our clients with clean, validated data at a scale and level of quality that wasn't achievable before.
Full sieve profile