Internal AI Job Board

Lead Product Manager - AI Developer Platform

Scribd - United States

This role is part of the Internal AI Job Board, a curated list of jobs for teams building or operating internal AI systems. Follow the original role page for the employer description, requirements, and application flow.

Responsibilities

• This is a foundational hire — Scribd's first product management role pointed inward, with the company itself as the customer. You'll lead Scribd's AI transformation, with the developer platform as the center of gravity: helping define the agentic engineering stack and shaping the evolution of the developer & agentic experience at Scribd. From that foundation, you'll extend the same thinking outward — partnering with Product, Design & Research, Data & Analytics, and TPM as they redesign their own work around AI agents, and guiding the internal app and agent platforms that every function depends on.
• To do this well, you'll spend real time with developers and eng teams — understanding how they work today, where the friction lives, and what the next leap looks like for them. You'll lead the product strategy for developer experience and how we measure it — quantitatively and qualitatively. You'll bring the same lens to the parallel transitions in other functions at Scribd. Across all of it, your job is to help us adopt AI thoughtfully and prove its impact — not to scale AI generation for its own sake, but to make every function more capable and more confident in the outputs.
• This role reports to the Director, Developer Platform, and partners closely with Engineering and Product leadership.
• Own the unified agentic enablement roadmap. Pull together the in-flight AI workstreams across Scribd into a cohesive, sequenced roadmap. Decide what is shared infrastructure and what stays function-specific. Make the tradeoffs visible to leadership.
• Be the product owner for the internal app and agent platforms. Define and drive the product strategy for the platforms the company depends on — the internal app platform, the plugin marketplace, the agent runtime — and make sure they meet each function's real needs. You will partner with the Developer Platform and Data Platform engineering teams who build the underlying capabilities.
• Translate between functions and the platform. Sit with different functions and their leaders to understand the workflows they are trying to redesign. Turn that into requirements we can build against, and turn the platform's capabilities into something every function can act on.
• Drive cross-functional commitments to closure. Our AI Enablement strategy includes several load-bearing pieces of shared infrastructure — canonical context, the evaluation practice, the plugin marketplace, the internal app platform, and a coherent context ownership map. Each has named owners across functions. Your job is to keep them moving in lockstep and surface the seams before they fragment.
• Make agentic enablement legible to the company. Define the metrics that matter — adoption, throughput, time recovered, evaluation quality, impact — and report against them. Build the narrative that gets every function bought in.
• Partner on go-to-market for internal users. Plan and run the rollouts, education, and feedback loops that drive measurable adoption — capabilities aren't done when they ship, they're done when each function depends on them. Treat internal rollouts with the same rigor we would treat an external launch.

Qualifications

• You are energized by hard, ambiguous, cross-functional problems where the technology landscape is moving faster than ever, adoption is uneven across functions, and the platform is actively evolving as it ships. You have spent enough time close to engineering to reason about platforms, APIs, and agent systems on their own terms — and enough time across functions to know that adoption is a people problem at least as much as a technical one. You are comfortable being the first PM in a new domain, defining the role as you go with no pre-existing playbook to inherit.
• You are all-in on AI reshaping every kind of knowledge work across a company, and you have already been applying it to your own. You think in systems and feedback loops, and you see the difference between how work happens on paper and how it actually gets done. You prefer structural guardrails over non-enforced guidelines. You'd rather ship and learn than wait for perfect, reserving real caution for the rare decisions that you can't easily reverse.

Apply on the original posting