СШАAI Product Manager, Agents & Orchestration
вчера
185k–260k USD / годСШАRemote
genai
Lead product development for AI agent orchestration and runtime layer in a fully remote role within the US.
Обязанности
- is building the AI-led GTM workforce: Superhumans that run SDR, SE, onboarding, account management and customer success work end to end, on top of a shared context graph that compounds with every customer conversation. Today each Superhuman mostly runs as a single agent on a single surface. The next product leap is orchestration: teams of agents that hand work to each other, run packaged GTM plays, and bring in humans only when the trust ladder says so.
- You'll own that leap. This is the first PM seat dedicated to the agent runtime and orchestration layer. You'll 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'll turn that into a platform that other PMs, forward-deployed engineers and customers build on.
- The window is short. Large platforms are consolidating the two ends of the funnel, and the live-conversation middle is still open. Expect to ship a v1 that isn't clean, then make it better every week.
- Own the agent runtime every Superhuman runs on: planning, tool calling, memory reads and writes against the context graph, guardrails, and the Assist, Co-pilot and Autonomous trust ladder. One runtime, 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. Packaged plays by funnel-stage outcome, like qualification and activation or retention and expansion, that customers configure instead of build. You define the primitives: triggers, steps, agent assignments and human checkpoints.
- Build and own the eval 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 you write the doc.
Будет плюсом
- Fine-tuning or RL exposure: SFT, DPO or reward-based training on outcome-labeled data, or per-tenant adapters.
- Voice or real-time conversational agents, where latency and interruption handling shape the product.
- GTM domain depth: you've carried a quota, run a CS book, or built for people who did.
- Workflow or orchestration products at the platform level.
- Enterprise platform experience: admin surfaces, permissions, audit, and the customers who ask for them.
- 's total compensation package is designed to be competitive and includes base salary, equity, and a full range of benefits and perks. Final compensation will depend on factors such as your skills, experience, qualifications, and location, and will be determined during the interview process. The hiring manager will share more details about the full compensation package and benefits as you move through the process.
- We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, notetaking, or summarizing responses. These tools assist our recruitment team but do not replace human judgment - all hiring decisions are made by people. If you would like more information about how your data is processed or prefer to opt out of any AI-assisted tools, please let your recruiter know. Opting out will not impact your experience or consideration.
Условия
- The salary range for this position is $185,000 to $260,000. The compensation package for this position also includes stock options and company-paid benefits.
Другое
- This is a fully remote opportunity and can be worked from any location in the United States.
- We are creating a new category of enterprise software: Autonomous Customer Experience. Our platform deploys Superhumans , go-to-market teammates with a face, a voice, and a GTM brain that serve the buyer across the entire journey with one continuous memory. They engage, demo, onboard, and support in the moments no company could ever staff, so the handoffs disappear and growth stops leaking.
- Join us as we define the category and build the platform that powers the next generation of revenue teams. When the buyer wins, the company wins. Every1 Wins.
- 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 with the agent stack: tool calling, planning loops, memory and retrieval, multi-agent handoff patterns, and the ways each one fails. You can read a trace and tell us what went wrong.
- You've built or owned an eval harness and used it to gate releases. You think in 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. We're not looking for SaaS feature PMs.
- Technical enough to spec data models, state machines and API shapes, and to explain the tradeoffs between them.
- Comfortable deciding with incomplete data and shipping weekly. You'd rather fix a v1 in production than polish a v2 in a doc.
- Clear writing. Your PRDs are short, your eval reports have numbers, and your Slack messages make sense to sales.