Staff Product Manager - Voice AI

Role Description

Voice is one of the highest-intent customer interaction channels for SMBs. Missed calls, delayed follow-ups, poor qualification, inconsistent appointment booking, and manual outbound outreach directly translate into lost revenue for local businesses and agencies.

HighLevel’s VoiceAI product is designed to change that. VoiceAI enables businesses to deploy AI-powered voice agents that can:

• Answer inbound calls
• Hold natural conversations
• Collect customer information
• Book appointments
• Trigger workflows
• Send post-call summaries
• Support both phone-based and browser-based voice experiences

The Staff Product Manager – VoiceAI will own the strategy, execution, and quality bar for one of HighLevel’s most important AI products. This role sits at the intersection of:

• AI agents
• Telephony
• CRM
• Workflow automation
• Knowledge retrieval
• Compliance
• Analytics
• User experience

You will be expected to deeply understand agencies, local businesses, call-heavy industries, and the real operational jobs that VoiceAI must perform:

• Answer calls
• Qualify leads
• Route conversations
• Book appointments
• Follow up
• Summarize outcomes
• Help businesses scale without adding headcount

This is not a generic AI PM role. This is a hands-on, technically fluent, customer-obsessed product leadership role for someone who can build a reliable, scalable, commercially meaningful AI voice product inside a complex, high-volume SaaS platform.

Qualifications

• 8–12 years of product management experience, with meaningful experience owning complex B2B SaaS, AI, communication, automation, CRM, or platform products.
• Prior experience as a Staff PM, Principal PM, Lead PM, or equivalent senior IC product role.
• Strong product judgment in AI-driven user experiences, especially where reliability, trust, latency, and outcome quality matter.
• Deep fluency in at least one of the following areas: voice AI, contact center software, telephony, conversational AI, workflow automation, CRM, customer communications, AI agents, or developer platforms.
• Technical enough to work credibly with engineering on LLM behavior, APIs, webhooks, telephony systems, latency, data pipelines, observability, model trade-offs, and scalability.
• Strong systems thinking: able to connect VoiceAI with contacts, calendars, conversations, workflows, opportunities, knowledge base, billing, permissions, and reporting.
• Clear understanding of SMB and agency-led SaaS models.
• Strong customer empathy and ability to translate messy real-world use cases into simple, scalable product experiences.
• Strong analytical ability. Can define metrics, diagnose funnel drop-offs, interpret product usage, and separate noisy feedback from real product problems.
• High ownership and execution velocity. Comfortable operating in a fast-growth, remote-first, high-context environment.
• Excellent written and asynchronous communication skills. Can write clear PRDs, crisp trade-off documents, launch notes, and executive updates.
• Comfortable making hard prioritization calls across customer asks, quality improvements, revenue opportunities, technical debt, and compliance constraints.

Requirements

• Define and drive the product vision, roadmap, and success metrics for HighLevel VoiceAI.
• Own the full VoiceAI product surface, including inbound calling, outbound calling, web/widget voice experiences, agent setup, prompt configuration, voice selection, call actions, testing, analytics, call logs, summaries, transcripts, and workflow integrations.
• Deeply understand customer use cases across agencies, SMBs, and high-call-volume verticals such as home services, medical spas, fitness, legal, real estate, insurance, dental, automotive, and local professional services.
• Translate customer problems into clear product requirements, PRDs, user stories, acceptance criteria, launch plans, and measurable success metrics.
• Partner closely with engineering on telephony architecture, LLM behavior, speech-to-text, text-to-speech, latency, call routing, retries, failure handling, observability, and infrastructure scalability.
• Work with design to simplify VoiceAI setup, agent testing, debugging, prompt management, call review, and performance reporting.
• Build strong integrations between VoiceAI, Contacts, Conversations, Calendars, Workflows, Opportunities, Payments, Knowledge Base, Custom Values, and AI Employee.
• Improve the quality of AI agent behavior through better prompts, guardrails, prompt evaluation, knowledge retrieval, call simulations, QA workflows, and customer feedback loops.
• Define instrumentation across activation, agent creation, test calls, phone number assignment, first successful call, call completion rate, booking rate, human escalation rate, workflow trigger rate, outbound answer rate, transcript quality, cost per successful outcome, revenue, retention, and support ticket trends.
• Partner with Product Marketing on positioning, GTM, use-case packaging, launch readiness, help docs, enablement, and agency-facing education.
• Partner with Support, Implementation, Trial Experience, Account Management, and Affiliates to identify friction, improve onboarding, reduce ticket volume, and make VoiceAI easier to sell, configure, and support.
• Own compliance-sensitive product decisions around outbound calling, consent, KYC requirements, acceptable use, timezone restrictions, call recording, transcripts, data handling, and abuse prevention.
• Use customer calls, support tickets, NPS/CSAT, ideas board feedback, usage analytics, and call quality reviews to continuously improve the product.
• Act as the product thought partner to engineering leadership for VoiceAI architecture, platform extensibility, reliability, and scale.

Benefits

• Agencies can launch useful VoiceAI agents for their clients quickly, without needing heavy technical setup or prompt engineering expertise.
• VoiceAI becomes a meaningful revenue and retention driver inside the broader AI Employee offering.
• Inbound VoiceAI reliably handles common SMB calls: FAQs, lead qualification, appointment booking, missed-call recovery, basic routing, and follow-up.
• Outbound VoiceAI becomes a scalable and trusted workflow action for follow-up, reactivation, reminders, and lead conversion.
• Customers see measurable business value: fewer missed calls, faster response times, more booked appointments, higher lead conversion, and reduced manual work.
• VoiceAI quality improves consistently across latency, naturalness, interruption handling, accuracy, escalation, summaries, transcripts, and workflow outcomes.
• Support tickets per active VoiceAI location decrease as setup, debugging, permissions, billing clarity, and documentation improve.
• VoiceAI becomes more extensible through APIs, webhooks, reusable templates, marketplace assets, and better developer/admin tooling.
• Internal teams clearly understand what VoiceAI does, how to sell it, how to support it, and how to measure success.
• You are viewed as the strategic and executional anchor for VoiceAI across Product, Engineering, Design, Product Marketing, Support, Trial Experience, Implementation, Account Management, Affiliates, and RevEx.

Ideal Candidate Profile

• Experience building AI agents, voice bots, virtual receptionists, IVR systems, contact center automation, or customer support automation.
• Experience with speech-to-text, text-to-speech, LLM orchestration, RAG/knowledge retrieval, prompt evaluation, call analytics, or AI quality evaluation.
• Experience with Twilio, LC Phone-like telephony systems, SIP, call routing, call recording, voicemail, number provisioning, A2P/10DLC, KYC, or telecom compliance.
• Experience building workflow automation products similar to HighLevel Workflows, Zapier, Make, HubSpot Workflows, Salesforce Flow, or customer journey builders.
• Experience with appointment-heavy verticals such as home services, healthcare/wellness, dental, legal, real estate, financial services, or local professional services.
• Experience with marketplace templates, reusable agent configurations, public APIs, developer platforms, or partner ecosystems.
• Experience with usage-based pricing, AI cost management, fair-use policies, or gross-margin-sensitive AI products.
• Hands-on familiarity with product performance metrics such as latency, completion rate, containment rate, escalation rate, answer rate, booking conversion, support ticket rate, P95/P99 performance, and cost per successful outcome.