Principal Product Manager, AI Workflows

Mural

2d•$210,120 - $262,650

About The Position

Mural’s AI Workflows team builds AI and agentic experiences that help teams reach decisions and follow through. The Principal Product Manager will own outcomes for new AI experiences from initial idea through demonstrated value, partnering with the Engineering Manager and Agentic Design Director as strategy counterparts. Working with engineers, ML engineers, designers, and product managers, the role moves ideas from hypothesis to prototype to evidence in weeks rather than quarters. The role involves building prototypes, testing with users, defining product-quality evaluations, and deciding whether experiences should ship, change, scale, or stop based on user behavior and evaluation results. It also includes model-selection decisions, attention to workflow economics, and designing agentic experiences for collaborative multiplayer sessions. The role works across product and business functions and shares effective practices across the product organization.

Requirements

At least 7 years of product management experience, including shipping at least one AI product from concept to real users.
Recent hands-on experience building agentic or LLM-based products, including learning from failures and making changes.
Technical fluency to design and interpret evaluations and work as a peer with engineering and ML engineering on context management, latency, reliability, and failure modes.
Sound judgment in comparing models, routing across them, adopting new models, and deciding when fine-tuning or a custom model is worthwhile.
Understanding of AI unit economics, including cost to serve, usage-based metering, and the effect of model choices on margin.
A track record of launching, rebuilding, changing direction, and stopping ideas based on evidence.
Comfort making high-stakes decisions with incomplete information and candidly discussing 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.

Nice To Haves

Experience with forward-deployed or customer-embedded product work.
Experience with agent interoperability, including MCP.

Responsibilities

Set AI Workflows strategy with engineering and design counterparts, own outcomes and supporting evidence, and engage deeply in the end-to-end user experience.
Identify team workflows where AI can add clear value in collaboration, alignment, and decision making.
Build working prototypes with modern AI tools and test ideas with users before committing further investment.
Frame releases as hypotheses with pass/fail criteria and decide whether to iterate, scale, or stop them.
Define product-quality evaluations with engineering to measure usefulness, trust, and reliability, and determine whether experiences meet the bar to ship.
Make model decisions with engineering and ML, including when to use frontier models, route across models, adopt new models, or build a custom model.
Understand workflow serving costs and work with growth and finance on usage metering and the unit of value.
Establish data loops, including decisions about what to collect, how to label it, and how it informs evaluations and model work.
Design agentic experiences for real multiplayer sessions so people can understand, steer, and correct agent behavior.
Talk with users weekly and incorporate what is learned into the work.
Align shipping decisions with design, engineering, marketing, sales, legal, and security teams.
Share learnings across the product organization so other teams can adopt effective practices.