Strategic Product Operations Lead

Fluency Digital, Inc.; San Francisco (CA); On-site; USD 90,000 - 120,000; Full time

Job summary

Fluency Digital, Inc. is seeking an experienced individual for a Product Operations role in San Francisco. Your responsibilities will include mapping preference frameworks and managing cross-functional sprints that involve engineering and customer research.

The ideal candidate should have over 1.5 years of relevant experience and be fluent in debugging ML pipelines. This role requires in-office collaboration 4+ days weekly, ensuring tight teamwork and execution.

Qualifications

• 1.5+ years in product operations, strategy, or technical program management at an AI, data, or developer-tooling company.
• Fluency in debugging misalignments in ML pipelines.
• Experience in designing processes from scratch.

Responsibilities

• Map how preference works and design operational rhythms.
• Own cross-functional sprints with research, engineering, and customers.
• Build feedback loops for model performance.

Skills

Product operations

Strategic execution

ML pipeline debugging

Designing operational processes

Collaboration

Job description

Data infrastructure startup building the preference layer for AI content

Your mornings start where product strategy meets messy reality. You'll map how preference actually works—translating subjective quality into structured signals that ML teams can build on—then design the operational rhythms that keep those pipelines alive. This means owning cross-functional sprints with research, engineering, and customers; building feedback loops that surface where models drift from human judgment; and writing the playbook for a new kind of data company. The Data & infra layer for preference doesn't exist yet. You'll help build it.

What we're looking for:

• 1.5+ years in product operations, strategy, or technical program management at an AI, data, or developer‑tooling company
• Track record of translating ambiguous strategic goals into concrete weekly execution across engineering and research teams
• Fluency in how ML pipelines break—enough to debug misalignments between model outputs and human evaluators without an engineering degree
• Experience designing operational processes from scratch, not just optimizing inherited ones; comfortable with zero-to-one documentation and tooling decisions
• Willing to be in‑office San Francisco or New York 4+ days weekly; this team builds through tight physical collaboration
• Must be based in the United States.