Product Builder (Product Manager), AI Agents

Apollo.io

About The Position

Apollo.io is the leading go-to-market solution for revenue teams, trusted by over 500,000 companies and millions of users globally, from rapidly growing startups to some of the world's largest enterprises. Founded in 2015, the company is one of the fastest growing companies in SaaS, raising approximately $250 million to date and valued at $1.6 billion. Apollo.io provides sales and marketing teams with easy access to verified contact data for over 210 million B2B contacts and 35 million companies worldwide, along with tools to engage and convert these contacts in one unified platform. By helping revenue professionals find the most accurate contact information and automating the outreach process, Apollo.io turns prospects into customers. Apollo raised a series D in 2023 and is backed by top-tier investors, including Sequoia Capital, Bain Capital Ventures, and more, and counts the former President and COO of Hubspot, JD Sherman, among its board members. Why this role exists The cost of going from idea to working prototype has collapsed. One person with taste, domain expertise, and AI tools can now do what used to require a full team and a sprint cycle. The PM role isn't disappearing, but the version of it that stops at specs and handoffs is. Apollo is hiring for what comes next. We call them Product Builders. People who carry practitioner-level GTM expertise, prototype fast, and close the loop from customer insight to production validation without waiting for someone else to move first. Smaller teams, fewer handoffs, end-to-end ownership. If that's how you already work, we should talk. Apollo's AI Assistant already has 21K weekly active users since GA in early March and helps users book 2x more meetings in their first 14 days. The platform powers over 564M AI prospecting actions annually. This role owns Autonomous AI Agents, the evolution of Apollo's AI Assistant into a goal-oriented, always-on direct report for every rep. You will define how the assistant orchestrates specialized agents, earns trust through progressive autonomy, and works where reps work across every channel they use.

Requirements

AI-native builder. You prototype with tools like Claude Code and Cursor and can show something you shipped, not just a spec you wrote.
High agency and strong ownership. You create clarity in ambiguous environments and push work forward without waiting for direction.
Product judgment. You focus on the highest leverage problems, make hard tradeoffs, and say no to good ideas to protect great ones.
Customer empathy. You understand how the people you are building for work day to day and optimize for their speed and confidence, not your own elegance.
Execution excellence. You define, ship, measure, and iterate without thrash. You treat shipping as the beginning of the work, not the end.
Technical fluency. You engage with engineering on performance, reliability, and integrations as a credible partner, not a passenger.
Domain expertise. You have lived the GTM motion you are building for. You have carried a bag, run the workflow, or built for the persona at depth.
Strong collaborator. You move fast and bring others with you. You communicate clearly, build trust quickly, and make the people around you better.
Experience shipping agentic AI products. You have put autonomous agents into production that handled real workflows and drove measurable business outcomes. Deep understanding of LLMs, RAG pipelines, agent frameworks, orchestration, trust models, failure handling, and evaluation of autonomous systems at scale. Bonus: you have built your own GTM agent.

Responsibilities

Agentic orchestration spanning scheduled, event-driven, and user-initiated execution modes, defining how the assistant continuously works on behalf of each rep across heartbeat routines, real-time signal responses, and direct requests.
Progressive autonomy framework that governs how the assistant earns trust over time, moving from fully supervised actions to autonomous execution within admin-defined boundaries.
Multi-channel delivery so the assistant works where reps work: Slack, iMessage, WhatsApp, email, SMS, and in-product, with daily briefs, hot lead alerts, draft outreach, meeting prep, and escalations.
AI quality and evaluation infrastructure across accuracy, relevance, and reliability. Nothing ships without hitting quality thresholds.
Go deep with customers. Understand their workflows through direct engagement and research, at the level of someone who has lived them. Use that understanding to set strategy and own your roadmap.
Prototype fast. Use Claude Code, Cursor, and other AI tools to validate ideas with something tangible before committing engineering resources. Ship to learn, then ship to scale.
Write clear specs and make hard tradeoffs. Define the quality bar. Partner with design on experience and with engineering on delivery. Stay in the loop, not just in the doc.
Define the trust model. Design the progressive autonomy system, how the assistant earns permission to act independently, how admins set boundaries, and how reps control their comfort level within them.
Build and scale evaluation infrastructure. Spend time in production data reviewing real agent sessions, identifying failure patterns in orchestration and execution, and turning them into systematic improvements.
Measure whether it worked. Work with analytics to track whether what you shipped changed behavior, including meetings booked, hours saved, time-to-first-win, and NRR impact. Feed what you learn back into the next cycle.
Drive alignment across GTM, Support, and cross-functional partners so what ships actually lands, meaning behavior changes, feedback loops close, and adoption follows.