Strategic Partnerships & Ops Lead, Physical AI

Sieve•San Francisco, CA

4d•Onsite

About The Position

Sieve is a multimodal lab that curates high-quality training datasets for AI, spanning video, audio, images, text, and 3D. They combine large-scale data infrastructure with novel multimodal understanding techniques to advance foundation models. This role is crucial for unlocking access to data from real industrial environments, such as factories and manufacturing lines, which is a key bottleneck for Physical AI development. The position involves building partnerships with industrial entities and establishing the operational framework to collect and process this data into lab-grade datasets. The role requires a blend of industrial partnership development and hands-on data operations, including managing workforces, ensuring quality assurance, and optimizing the data platform. Ideal candidates will have experience in industrial consulting, robotics operations, private equity operations, or early-stage operations roles with experience in managing human-in-the-loop work and building trust with plant leaders.

Requirements

Direct relationships into factories, manufacturing, or adjacent industrial environments, or a proven track record of building them.
Mixed technical and non-technical skillset; comfortable with data tooling, light scripting, and spreadsheet-level analysis.
Strong organizational skills and attention to detail; able to manage multiple concurrent work streams.
Growth mindset: experience running or contributing to user acquisition, sourcing campaigns, or platform growth.
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.
Must be able to work in-person at our SF HQ.

Nice To Haves

Experience managing human-in-the-loop data operations or annotation pipelines.
At least 1 year of engineering experience or strong technical fluency.
Prior work inside manufacturing, robotics deployment, factory ops, PE portfolio ops, or as an early hire / AI-lab ops lead.
Familiarity with data quality frameworks, ML data pipelines, or physical AI.

Responsibilities

Identify, pitch, and close partnerships with factories, manufacturers, and industrial operators.
Navigate security and compliance processes from initial contact to live data collection.
Manage workforce, task assignment, and QA workflows for the data operations platform.
Develop Standard Operating Procedures (SOPs) and training to maintain data quality for frontier labs.
Source, onboard, and manage a distributed workforce for annotation, curation, and review.
Test and scale acquisition and sourcing channels for data.
Collaborate with engineering on product operations, shipping tooling improvements, tracking operational metrics, and addressing data platform gaps.
Document playbooks to standardize partner wins and operational workflows for repeatability across sites and programs.