PolyAI is the Agentic Dialog Platform for building the conversational enterprise, used to build, run, govern, and improve dialog agents at scale. Powered by Raven, a proprietary dialog model trained on more than a billion enterprise conversations, PolyAI agents are built for complexity, open at every layer, and sovereign by design. PolyAI serves hundreds of enterprises across 75 languages and 25 countries, including Metro Bank, Marriott, PG&E, and UniCredit.

PolyAI automates customer service through lifelike voice assistants that let customers lead a conversation. Our voice assistants make it possible for businesses to deliver outstanding customer service that rivals their human agents.

Our customers, which include the world’s leading logos, are expanding how they use our platform, driving automation of critical customer service operations and integrating PolyAI into their daily customer service workflows.

We’re looking for a highly technical, senior product leader to own the platform underneath everything our agents do: the runtime that serves conversations in production, the inference layer that powers our models, and the data infrastructure that feeds both. This is a deep infrastructure role with direct exposure to latency, cost, and reliability tradeoffs at scale, not a roadmap-and-backlog role. You’ll set strategy for areas other teams build on top of, working closely with engineering leadership rather than through them

RESPONSIBILITIES

Own the product strategy for runtime (how conversations get served in production), inference (how our models are hosted and served, including self-hosted and third-party paths), and the data platform (how conversation and model data moves, is stored, and is queried across the business)
Set direction on build-vs-buy and self-host-vs-managed tradeoffs for model serving, including evaluating cost, latency, and scaling implications of infrastructure decisions before they’re made, not after
Build and own the evaluation framework for how we measure runtime and inference performance in production (latency, throughput, cost per minute/token, failure rates), and drive continuous improvement off that data
Partner directly with infrastructure and platform engineering leadership to sequence a technical roadmap that often has no visible customer-facing surface, and defend its priority against features that do
Own how conversation data flows into and out of the platform, including data residency, retention, and access-control tradeoffs that affect what the business can build or sell
Translate ambiguous technical constraints (capacity limits, model failure modes, data gaps) into decisions the rest of the business can plan around
Lead and influence a cross-functional group of engineers, data scientists, and infrastructure specialists without formal authority over them
Represent this area in exec-level conversations on cost, capacity planning, and platform risk

REQUIRED QUALIFICATIONS

6+ years of product management or related technical experience, including a strong track record owning infrastructure, ML/data platform, or runtime-adjacent products, not just user-facing features
Bachelor’s degree (or foreign equivalent) in Computer Science, Engineering, Statistics, or a related field involving AI/ML or distributed systems, or equivalent industry experience
Strong working knowledge of how inference and model-serving infrastructure actually works: latency budgets, GPU/node economics, self-hosted vs. managed serving tradeoffs
Comfortable owning a roadmap with no end-user-visible surface, and able to make the business case for infrastructure investment on its own terms
Experience with data platform decisions: pipelines, residency/compliance constraints, and tradeoffs between centralizing vs. fragmenting data by region or product line
Demonstrated ability to operate at a senior level: setting direction, not just executing against someone else’s plan, and mentoring more junior PMs
Entrepreneurial mindset, comfortable with ambiguity and fast iteration in a high-growth environment