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Sieve

Cloud platform for AI video/audio apps

Forward Deployed Engineer - Physical AI

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA$150k - $350k /yr

About the Job

Requirements

  • Comfort working directly with customers or external teams to translate ambiguous needs into concrete technical systems.
  • Strong Python development skills and hands-on experience with PyTorch or similar machine-learning frameworks.
  • Experience building custom algorithms, model workflows, or large-scale data pipelines.
  • Strong intuition for dataset quality, filtering, labeling, evaluation, and edge cases.
  • Ability to break customer-level goals down into the models, heuristics, infrastructure, and quality-assurance steps needed to deliver.
  • Ability to write clean, maintainable code and move quickly without creating brittle systems.
  • In-person attendance at the San Francisco headquarters, onsite five days per week.

Responsibilities

  • Work on highly specific dataset problems for frontier AI labs.
  • Work closely with customers and internal teams to understand data needs and translate ambiguous requirements into production systems.
  • Build systems to find, generate, filter, transform, evaluate, and package high-quality video datasets at scale.
  • Deliver end-to-end outcomes.

Desired Qualifications

  • Experience with large-scale video, audio, or multimodal data processing.
  • Active contribution to open-source projects.
  • Experience as an early hire at a startup.

About the Company

Sieve

Cloud platform for AI video/audio apps

SieveData offers a cloud platform to build and deploy custom AI-powered video and audio features, providing ready-made models and the deployment infrastructure needed by product teams. Developers integrate capabilities via APIs to add functions such as automatic video cropping, translation, speech transcription, and noise removal, with each app including an auto-generated playground for testing and collaboration; the platform auto-scales with demand and uses a compute-based pricing model. It differentiates itself by pairing ready-to-use models with end-to-end infrastructure and a per-app playground, emphasizing ownership and straightforward collaboration. The goal is to help product teams quickly create, test, and deploy scalable AI video and audio features while maintaining cost visibility and control.

Company size
11-50
Company stage
Seed
Total funding
$4.1M
Headquarters
San Francisco, CA
Founded
2021

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