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Principal Product Manager, AI Workflows

AI Summary
Mural is seeking a Principal Product Manager to own strategy, discovery, validation, and delivery for AI-powered collaboration workflows. The role focuses on agentic and LLM-based product experiences, rapid prototyping, model selection, product-quality evaluations, and AI unit economics. This leader will partner closely with engineering and design counterparts while making evidence-based decisions to iterate, scale, or stop initiatives. Success requires substantial hands-on AI product experience, strong technical judgment, and the ability to align product, engineering, ML, growth, legal, security, marketing, and sales stakeholders.
Remote from
USA
Salary
USD 210,120–262,650 / yr
Employment
Full Time
Experience
Director
Published
Apply before
3 Dec 2026
Listing views
25
Application actions
2
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Opportunity details

About this role.

ABOUT 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.

YOUR 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.

WHAT YOU’LL DO

  • 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

  • Find the team workflows where AI adds clear value, at the intersection of collaboration, alignment, and decision making.

  • Build working prototypes with modern AI tools (Claude, Cursor, Codex, or similar) to test ideas with users before committing further investment.

  • Turn each release into a hypothesis with pass/fail criteria, and make the call on whether to iterate, scale, or kill it.

  • 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.

  • 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.

  • 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.

  • Set up the data loops that improve quality, including what we collect, how we label it, and how it feeds evals and model work.

  • Design agentic experiences that work in real multiplayer sessions, where people need to understand, steer, and correct what the agent does.

  • Talk with users every week and bring what you learn back into the work.

  • Work with design, engineering, marketing, sales, legal, and security teams to align on shipping decisions.

  • Share what you learn across the product organization so other teams can adopt the practices that work.

WHAT YOU’LL BRING

  • 7+ years in product management, including shipping at least one AI product from concept to real users.

  • Recent hands-on experience building agentic or LLM-based products, including what went wrong and what you changed.

  • 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.

  • 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.

  • Understanding of AI unit economics, including cost to serve, usage-based metering, and how model choices change margin.

  • A record of launching, rebuilding, changing direction, and killing ideas based on evidence

  • Comfort making high-stakes decisions with incomplete information, and being candid about the tradeoffs.

  • Experience owning outcomes while partnering with expert engineering and design leaders as true counterparts.

  • Experience creating structure where no playbook exists.

  • Experience with collaboration or productivity software.

  • Experience with forward-deployed or customer-embedded product work.

  • Experience with agent interoperability, including MCP.

Equal 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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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a principal-level role requiring ownership of ambiguous, high-stakes AI product bets from concept through measurable customer value. It combines strategic leadership with hands-on prototyping, LLM evaluation, model trade-off decisions, and cross-functional alignment.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$236,385
US market range (per year)$210k–$280k
AI insightThe employer discloses a yearly base compensation range of $210,120–$262,650 USD, with a midpoint of $236,385. This is credible for a US-based principal AI product management role. Estimated US market compensation for comparable roles is approximately $210,000–$280,000 USD per year; this is an AI estimate, not verified salary research.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an AI product you took from concept to launch. How did you validate that it solved a real customer problem?

I started with a narrowly defined user workflow and interviewed target users to understand current pain points and desired outcomes. I built a lightweight prototype, defined behavioral success metrics and qualitative trust signals, then tested it with users before committing engineering investment. After launch, I monitored adoption, task success, feedback, and failure patterns to guide iterations.

How would you decide whether an AI workflow is ready to ship?

I would define explicit pass/fail criteria before development, including task usefulness, reliability, latency, safety, cost to serve, and user understanding of the agent’s actions. I would combine offline evaluation results with real-user testing and segment performance by important workflow types. If the experience fails a critical trust or quality threshold, I would delay launch, narrow its scope, or redesign it.

How do you choose among frontier models, model routing, fine-tuning, and building a custom model?

I compare options against the workflow’s quality requirements, latency, reliability, privacy constraints, operational complexity, and unit economics. I use benchmark tasks and production-like evaluations rather than selecting based solely on broad model claims. Routing is useful when different tasks need different strengths, while fine-tuning or custom models should be justified by durable performance or cost advantages that exceed their maintenance burden.

Tell us about a time you stopped or significantly changed a product initiative based on evidence.

I would explain the original hypothesis, the measurable criteria established in advance, and the evidence showing that the expected value was not materializing. I would communicate the decision clearly to stakeholders, preserve reusable learning or technical assets, and redirect the team toward the highest-confidence opportunity. The goal is to treat stopping as disciplined resource allocation rather than failure.

How would you design an agentic experience for a live multiplayer collaboration session?

I would make the agent’s intent, context, proposed actions, and confidence visible so participants can understand and guide it. The experience should provide clear approval, correction, undo, and escalation mechanisms, especially when actions affect shared work. I would test the design with real groups to ensure it improves alignment without disrupting facilitation, ownership, or trust.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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