San Francisco, CA

On-site
Full-time Compensation: $150,000–$250,000 + equity

About The Company

An AI data-infrastructure company building custom algorithms, models, and data pipelines at scale across video, audio, and multimodal data for AI training and evaluation. The team works on highly specific dataset problems for frontier AI labs and enterprise customers, and its work ships directly into the models defining the frontier. Small, capital-efficient team moving fast.

Founded 2022

:25 people (Seed)
Industry: AI Tools / AI data infrastructure

The Role

Own the day-to-day execution and scaling of the company's data-operations platform — the human workforce, vendor partnerships, and QA processes that power it. A scrappy, hands-on role at the intersection of operational execution and platform growth: run acquisition campaigns to expand the user base, build and improve workflows from scratch, and work directly with engineering to ship tooling improvements. Best fit is someone early in their career with strong technical instincts, high communication skills, and startup experience who can move fast without a playbook.

What You'll Be Doing

Operate and scale the internal data-ops platform, including workforce management, task assignment, and QA workflows
Drive platform growth by running acquisition campaigns, testing new sourcing channels, and expanding the user base
Source, onboard, and manage a distributed human workforce for data annotation, curation, and quality review
Build and improve QA processes to meet frontier AI lab standards
Own product ops for the data platform — work with engineering to ship tooling improvements, track metrics, and identify gaps
Create documentation, SOPs, and training materials for operational workflows

Tech stack: Data tooling, light scripting, and spreadsheet-level analysis; familiarity with ML data pipelines a plus.

Requirements

Operate and scale internal data ops platform including workforce management and QA workflows
Drive platform and partnerships growth through acquisition campaigns and sourcing channels
Source, onboard, and manage distributed human workforce for data annotation and curation
Build and improve QA processes for frontier AI lab standards
Own product ops for data platform and work with engineering on tooling improvements
Create documentation, SOPs, and training materials for operational workflows
Mixed technical and non-technical skillset with comfort in data tooling and light scripting
Strong organizational skills and attention to detail managing multiple concurrent work streams
1 to 5 years experience, startup environment preferred
SF in-person, full-time

Green Flags

Experience managing human-in-the-loop data operations or annotation pipelines
At least 1 year of engineering experience or strong technical fluency
Early hire experience at startup or ops leadership at AI lab
Familiarity with data quality frameworks or ML data pipelines
Data space background a strong plus

Red Flags

No technical grounding whatsoever
Strategy-only profile with no hands-on execution
No startup exposure

Why Join

Ship into frontier models — your operational work directly powers datasets frontier AI labs and top generative-AI companies train on
True 0→1 ownership — build workforce, QA, and growth workflows from scratch with real end-to-end ownership
High-leverage seat on a small, capital-efficient team reporting into the founding team
Strong comp and perks: competitive equity, 401k, full health insurance, all meals covered, Ubers home

Details

Location — San Francisco, CA
Work policy — On-site (in-person at HQ)
Compensation — $150,000–$250,000 + equity
Visa sponsorship — H-1B, OPT
Employment type — Full-time

David Joseph & Company

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