Principal Product Manager, AI Workflows
Mural has an opening for a Principal Product Manager, AI Workflows as a remote role open to applicants in United States. The listing describes it as a full-time, staff/principal-level position.
DGJ summary of this job
Our own short read of this listing, built only from the facts it gives. The employer’s original text follows further down.
Mural has an opening for a Principal Product Manager, AI Workflows as a remote role open to applicants in United States. The listing describes it as a full-time, staff/principal-level position.
The advert names LLM as a skill for this role.
The employer does not state a salary in this listing; DGJ Insight below shows an official pay benchmark for the place where one exists.
Dev Global Jobs found this job on Jobicy on Nov 3, 2026.
- Employer
- Mural
- Location
- Remote (United States)
- Job type
- Full-time
- Level (from the title)
- staff/principal-level
- Pay
- Not stated by the employer
- Found
- Nov 3, 2026 on Jobicy
- Employer text
- 617 words, about 3 min to read
- Apply only on the official page (jobicy.com). Dev Global Jobs is not the employer and never asks for a fee.
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DGJ Insight
Our own data about this role and place, from official statistics and our live job index.
Source: OECD Average annual wages, 2025
About the role
Full text from the employer, as published on Jobicy, arranged into sections. Nothing added or removed.
01ABOUT THE TEAM
Mural helps teams think, decide, and follow through together. AI made producing content cheap. It did not make agreement, commitment, or follow-through any easier. The AI Workflows team builds the AI and agentic experiences inside Mural that help teams reach decisions and keep them.
02YOUR MISSION
You will own the outcomes for new AI experiences from first idea to proven value. You will partner with the Engineering Manager and the Agentic Design Director for AI Workflows as counterparts, and together you will own the strategy. You will work with a wider team of engineers, ML engineers, designers, and product managers, moving from hypothesis to working prototype to evidence in weeks, not quarters. You will build prototypes yourself and decide what ships, what changes, and what we stop, based on what users do and what the evals tell us.
03WHAT YOU'LL DO
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Set strategy for AI Workflows together with your engineering and design counterparts, own the outcomes and the evidence behind what we build, and engage deeply in the end-to-end user experience
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Find the team workflows where AI adds clear value, at the intersection of collaboration, alignment, and decision making.
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Build working prototypes with modern AI tools (Claude, Cursor, Codex, or similar) to test ideas with users before committing further investment.
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Turn each release into a hypothesis with pass/fail criteria, and make the call on whether to iterate, scale, or kill it.
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Define product-quality evals with engineering that measure usefulness, trust, and reliability, and own the call on whether an experience meets the bar to ship.
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Decide with engineering and ML when to use frontier models, when to route across models, when to bring in a new model, and when a model of our own is worth building.
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Understand the cost to serve each workflow, and work with growth and finance on how usage is metered and what the unit of value is.
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Set up the data loops that improve quality, including what we collect, how we label it, and how it feeds evals and model work.
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Design agentic experiences that work in real multiplayer sessions, where people need to understand, steer, and correct what the agent does.
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Talk with users every week and bring what you learn back into the work.
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Work with design, engineering, marketing, sales, legal, and security teams to align on shipping decisions.
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Share what you learn across the product organization so other teams can adopt the practices that work.
04WHAT YOU'LL BRING
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7+ years in product management, including shipping at least one AI product from concept to real users.
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Recent hands-on experience building agentic or LLM-based products, including what went wrong and what you changed.
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Technical fluency to design and read evals, and to work as a peer with engineers and ML engineers on context management, latency, reliability, and failure modes.
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Sound judgment on model decisions: comparing models, routing across them, adopting new ones, and deciding when fine-tuning or a custom model is worth it.
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Understanding of AI unit economics, including cost to serve, usage-based metering, and how model choices change margin.
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A record of launching, rebuilding, changing direction, and killing ideas based on evidence
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Comfort making high-stakes decisions with incomplete information, and being candid about the tradeoffs.
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Experience owning outcomes while partnering with expert engineering and design leaders as true counterparts.
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Experience creating structure where no playbook exists.
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Experience with collaboration or productivity software.
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Experience with forward-deployed or customer-embedded product work.
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Experience with agent interoperability, including MCP.
05Equal Opportunity
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
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