Principal Product Manager- Conversational AI

Own the roadmap for the customer-facing conversational agent across chat and voice, from intent coverage and conversation design to the interfaces partners embed in their apps and websites

Set the product bar for voice specifically, covering latency, turn-taking, barge-in handling, localization, realtime voice and ASR/TTS quality, since a caller's tolerance for a bad interaction is lower than a chat user's

Engage enterprise partners through discovery, requirement shaping, and launch decisions, translating partner asks into product choices that keep one product serving many partners

Partner with Partner Solutions on implementation and onboarding

Drive design daily, working hands-on in Figma to produce flows and wireframes, and partner with design on high-fidelity and production assets

Build working prototypes using AI coding tools where they beat a written spec, to test conversation flows and interface ideas

Define and defend quality bars with the evaluation team, covering resolution rate, containment, escalation correctness, and conversation quality across both chat and voice

Specify agent behavior in detail, including resolution, tone, escalation policy, guardrails, and edge cases, and iterate from production conversation data

Stay close to GTM, Partner Solutions, and Success teams, feeding what the field learns back into the roadmap

8+ years of product management experience, including principal or lead-level ownership of a customer-facing product, with direct experience shipping voice-based conversational products such as IVR, voice bots, or contact center voice automation

Hands-on ownership of a production voice product, not only chat or text-based conversational AI, with working knowledge of ASR/TTS tradeoffs, latency budgets, and turn-taking or barge-in design. This is a required skillset

Design fluency, comfortable manipulating design files and producing flows and basic mocks solo in Figma, having operated as a de facto design lead on a lean team

The ability to write enough code to get a prototype working without waiting on an engineer, treating AI tools as a normal part of how they build

Hands-on front-end experience, having built or shipped web front ends and able to engage engineers credibly

Deep partner-facing experience with enterprise clients, ideally in regulated industries, shaping requirements and launch decisions at the product level

A data-driven approach grounded in customer service and automation metrics, including resolution, containment, CSAT, and escalation quality

Working familiarity with LLM-based products, including prompt and behavior design, AI quality practices, and their failure modes