Sieve
Strategic Partnerships & Ops Lead, Physical AI
San Francisco
Sponsorship not specifiedDetected 18 days ago
SwiftMachine LearningData EngineeringRoboticsOrganizational Skills
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odds of building a lasting career here
34Risky
Cap-exempt (no lottery)0
Sponsors this role70
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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Files H-1B transfers
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About the role
- This role mixes industrial partnerships with hands-on data ops.
- You'll open doors into factories and similar environments, then run the workforce, QA, and platform work that turns that access into reliable multimodal data for frontier labs.
- Strong fits look like industrials consultants, robotics operators, PE ops folks, or early ops hires who've already managed human-in-the-loop work and know how to earn trust with plant leaders and ops teams.
Responsibilities
- build SOPs and training so quality stays at the bar frontier labs require.
- Grow supply and partnerships: Source, onboard, and manage a distributed workforce for annotation, curation, and review
- Own product ops with engineering: Ship tooling improvements, track operational metrics, and close gaps in the data platform.
- Turn one-offs into systems: Document playbooks so partner wins and ops workflows repeat across sites and programs.
- Operate the data ops platform: Run workforce management, task assignment, and QA workflows; build SOPs and training so quality stays at the bar frontier labs require.
- Grow supply and partnerships: Source, onboard, and manage a distributed workforce for annotation, curation, and review; test acquisition and sourcing channels that scale.
- Run workforce management, task assignment, and QA workflows; build SOPs and training so quality stays at the bar frontier labs require.
- Source, onboard, and manage a distributed workforce for annotation, curation, and review; test acquisition and sourcing channels that scale.
- Own product ops with engineering:
- Document playbooks so partner wins and ops workflows repeat across sites and programs.
Requirements
- Comfort with ambiguous, relationship-heavy work and the unglamorous ops needed to make a partnership live
- Bachelor's in CS, STEM, or equivalent practical experience
Nice to have
- Experience managing human-in-the-loop data operations or annotation pipelines
- At least 1 year of engineering experience or strong technical fluency
- Familiarity with data quality frameworks, ML data pipelines, or physical AI
Benefits
- 401(k) and full health insurance
Company info
- Sieve is a multimodal lab curating the world's highest-quality training datasets, spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure with novel multimodal understanding techniques to push the frontier of foundation models. We exist to solve one of the biggest bottlenecks in AI: high-quality training data.
- We partner with top AI labs and did $XXM last quarter alone, with a team of roughly 40 people. We raised our Series A from firms including Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
- Physical AI needs data from real industrial environments - factories, manufacturing lines, and operations that don't show up on the open web. Unlocking that access, and running the ops that turn it into lab-grade data, is a core growth constraint. You'll join early enough to build both the partnerships and the operating system behind them.
- Sieve is a multimodal lab curating the world's highest-quality training datasets, spanning video, audio, images, text, and 3D.
- We combine exabyte-scale data infrastructure with novel multimodal understanding techniques to push the frontier of foundation models.
- We exist to solve one of the biggest bottlenecks in AI: high-quality training data.
- We partner with top AI labs and did $XXM last quarter alone, with a team of roughly 40 people.
- We raised our Series A from firms including Matrix Partners, Swift Ventures, Y Combinator, and AI Grant.
- Physical AI needs data from real industrial environments - factories, manufacturing lines, and operations that don't show up on the open web.
- Unlocking that access, and running the ops that turn it into lab-grade data, is a core growth constraint.
- You'll join early enough to build both the partnerships and the operating system behind them.
- We exist to solve one of the biggest bottlenecks in AI:
- high-quality training data.
This listing is sourced directly from Sieve's careers page and normalized into a canonical job model.