AI Product Manager, Agents & Orchestration

1mind is building the next generation of revenue teams, creating a new category of enterprise software: Autonomous Customer Experience. Their platform deploys Superhumans, AI-powered GTM teammates with a face, voice, and GTM brain that serve buyers across the entire journey with continuous memory. These Superhumans engage, demo, onboard, and support in moments no company could staff, eliminating handoffs and preventing growth leaks.

Join 1mind as they define this category and build the platform that powers the next generation of revenue teams. Their mantra is: When the buyer wins, the company wins. Every1 Wins.

About the Role

1mind is constructing an AI-led GTM workforce: Superhumans that handle SDR, SE, onboarding, account management, and customer success work end-to-end, leveraging a shared context graph that compounds with every customer conversation. Currently, each Superhuman operates mostly as a single agent on a single surface. The next product leap is orchestration: teams of agents that hand off work, run packaged GTM plays, and involve humans only when necessary.

You will own this leap. This is the first PM role dedicated to the agent runtime and orchestration layer. You will work directly with the GenAI team, report to the Chief Product Officer, and decide how agents plan, call tools, share memory, hand off, and get evaluated. Then, you will turn these decisions into a platform that other PMs, forward-deployed engineers, and customers can build on.

The window for this opportunity is short. Large platforms are consolidating the ends of the funnel, and the live-conversation middle is still open. Expect to ship a v1 that isn’t clean, then improve it every week.

What You’ll Do

• Own the agent runtime every Superhuman runs on, including planning, tool calling, memory reads and writes against the context graph, guardrails, and the Assist, Co-pilot, and Autonomous trust ladder. One runtime for many roles.

• Design multi-agent orchestration: handoffs between Superhumans across the customer journey (inbound SDR to SE Ride-Along to onboarding to CSM), shared state across a deal or account, and conflict resolution when two agents want to act on the same contact.

• Turn GTM orchestrations into a product. Create packaged plays by funnel-stage outcome, such as qualification and activation or retention and expansion, that customers configure instead of build. Define the primitives: triggers, steps, agent assignments, and human checkpoints.

• Build and own the evaluation harness for agent behavior, including task-level success, objection-to-advance rate, escalation precision, and hallucination and tool-error rates. Set the bar that gates every release and own the dashboard that tracks it.

• Define the platform surface: the APIs, configuration, and admin experiences that app-layer PMs, forward-deployed engineers, and enterprise admins use to compose and tune agents, including per-enterprise adapters and fine-tuning hooks.

• Run a weekly ship cadence with GenAI and engineering, with enablement and customer success looped in before every release.

• Write the specs engineers want to build from (data models, state machines, API shapes, and the tradeoffs between them), and prototype in code or no-code tools before writing the documentation.

What You’ll Bring

Required:

• 5+ years of product management, including at least 2 years shipping LLM-based agents to production users. You’ve owned a product where the model’s behavior was the product.

• Hands-on experience with the agent stack: tool calling, planning loops, memory and retrieval, multi-agent handoff patterns, and understanding how each can fail. You can read a trace and diagnose issues.

• Experience building or owning an evaluation harness and using it to gate releases. You think in terms of task success rates, not vibes.

• Experience at an AI-native company or on a large platform team paired with founder or early-stage startup experience, preferred. Not looking for traditional SaaS feature PMs.

• Technical enough to specify data models, state machines, and API shapes, and explain the tradeoffs between them.

• Comfortable making decisions with incomplete data and shipping weekly. You prefer fixing a v1 in production over polishing a v2 in a document.

• Clear writing. Your PRDs are concise, your eval reports have numbers, and your Slack messages are understandable to sales.

Preferred:

• Fine-tuning or RL exposure: SFT, DPO, or reward-based training on outcome-labeled data, or per-tenant adapters.

• Experience with voice or real-time conversational agents, where latency and interruption handling shape the product.

• Depth in GTM domain: you’ve carried a quota, run a CS book, or built for people who did.

• Experience with workflow or orchestration products at the platform level.

• Enterprise platform experience: admin surfaces, permissions, audit, and the customers who ask for them.