Senior Product Manager

๐Ÿข We Are Rosie ยท all We Are Rosie jobs
๐Ÿ“ United States
๐Ÿ“… Posted 2026-08-16 ยท via Himalayas
๐Ÿท Product-Management,Senior-Product-Management,AI-Product-Management,Platform-Product-Management,Marketplace-Product-Management,Senior-Product-Manager-UX-UI,Senior-Product-Lead,Product-Manager
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About We Are Rosie

We Are Rosie is redefining how marketing talent works โ€” connecting exceptional freelance marketers ("Rosies") with the world's most ambitious brands. Our platform is the engine behind this mission: matching talent, enabling seamless collaboration, and giving Rosies the infrastructure to do their best work. The product team is responsible for making that engine run.
The Role

We're looking for a Senior Product Manager who is ready to own. You'll lead end-to-end delivery of core platform initiatives, from discovery through launch through measurement. You're the connective tissue between engineering, design, operations, and the business โ€” and you know how to make things happen in a fast-moving, resource-constrained environment.

A significant part of this role involves AI. You'll ship product features built on large language models, and you'll use AI to improve how internal teams work. We're looking for someone with production experience (internal tools included!), not just familiarity with the tools.

This is a high-accountability, high-execution builder role. You'll set direction for your area, prioritize ruthlessly, and ship work that moves the business.
Responsibilities

- Product area leadership โ€” Full accountability for your work stream: roadmap, priorities, tradeoffs, and results.

- Discovery and delivery โ€” Run structured discovery cycles (user research, data analysis, stakeholder input) and translate insights into clear, actionable specs and prototypes. Collaborate with engineering to scope, estimate, and ship on time.

- AI product delivery โ€” Ship features built on LLMs and agentic workflows, and own the tradeoffs underneath them: model selection and migration, prompt and context design, token cost per transaction, latency, evaluation, and failure modes. You can explain to Finance what a feature costs to run per user, and to Engineering why one model fits a given job better than another.

- AI for internal operations โ€” Identify manual, spreadsheet-bound work across Rosie Ops, Client Experience, and Finance, and replace it with automations and agents. Success is measured in hours returned, error rates reduced, and single-person dependencies removed.

- Metrics ownership โ€” Define success metrics for your initiatives and track them. You can speak confidently to the why behind prioritization decisions using data.

- Stakeholder alignment โ€” Build trust and earn alignment with cross-functional partners (Sales, Marketing, Operations, Client Experience, and Finance). You manage up, sideways, and across โ€” proactively, not reactively. You set the cadence and rhythm of communication; stakeholders know what to expect and when, without asking.

- Platform stewardship โ€” Deeply understand the WRR marketplace โ€” both Rosie experience and brand-side workflows โ€” and make decisions that serve both sides of the market.

Requirements

- Product management foundation โ€” 2โ€“5 years of product management experience, ideally with marketplace, platform, or two-sided network products.

- A product loop you built yourself โ€” Discovery โ†’ prototype โ†’ validate with users โ†’ ship โ†’ measure โ†’ feed what you learned into the next cycle. We want to hear about the loop, not one launch: how fast it turns, what you changed after the data came back, and what you'd do differently on the third pass.

- AI features shipped to production โ€” We want to hear about something customers or colleagues actually used: what the model was doing, why you selected it, what you tried that didn't work, what it cost to run at volume, and what it changed for the business. Specifics matter more than scale โ€” "reduced match-review time from 40 minutes to 6 across 300 weekly reviews" is the level of detail we're looking for.

- Fluency in the mechanics โ€” Context windows, token cost and how to reduce it, prompt engineering vs. retrieval vs. fine-tuning, when an agent loop is worth the added complexity, how to evaluate output quality at scale, and how to handle inac

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