Staff Product Manager, AI Assistant

Calendly•San Francisco, CA

2d•Hybrid

About The Position

Millions of people already rely on Calendly, and we’re still in the midst of exciting product growth — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey. This is a hybrid position located in the Bay Area; the expectation is to go into the San Francisco office 2 times per week. Calendly is used by more than 20 million people to take the work out of scheduling. We're now building the layer that does the work around the meeting too: the prep before it, the follow-up after it, and the next step a professional would otherwise do manually. This role owns the assistant that surfaces and drafts those actions, and the quality that makes people trust them.

Requirements

A minimum of 8-10 years of varied PM experience across both startups and scaled companies; bonus points if you've been a founder
Experience with assistant or agent products, or with AI-generated content where voice and tone mattered
Real taste for AI output quality
Keen product sense and UX design experience, with deep empathy for customers and a drive to build delightful, frictionless experiences
Experience building 0-to-1 products
The ability to work in our San Francisco office 1-3 times per week
Authorized to work lawfully in the United States of America, as Calendly does not engage in immigration sponsorship at this time

Responsibilities

Understand the work that surrounds meetings for our users: what they're trying to accomplish, where they lose time, and which of those problems are worth solving and how to solve them simply enough that users feel the help without the overhead.
Turn those problems into an assistant that helps at the right moments
Earn enough trust in the assistant's output that users are willing to hand it more autonomy over time, letting it act without reviewing every step.
Ship fast and learn in the open: get real versions in front of users, watch how they respond, and iterate quickly.