Product Manager, AI Models - Descript

Descript ’s vision is to put video in every communicator’s toolkit. Back in the day you needed like six monitors and a bachelor’s degree to edit video. Descript lets you do it by editing docs & slides, and increasingly by just asking AI. In the future, maybe you won’t even need to ask! But building a new way to record or generate (or both!) videos that look & sound good comes with a series of unique design, technology, and business challenges. In other words, we need really good product managers.

We’re looking for a Product Manager to help build the future of video editing with AI. You’ll work alongside a small, flat, highly collaborative team of experienced PMs, AI researchers, engineers, designers, and marketers. This is an opportunity to get hands-on experience with cutting-edge AI technology in a product users love and grow fast in your PM craft.

We're looking for a Product Manager to lead the AI Research and Enablement roadmap at Descript . This role sits at the intersection of cutting-edge AI research, production ML infrastructure, and product strategy. You'll be responsible for ensuring our AI capabilities are best-in-class while enabling our product teams to ship AI-powered features that delight users.

Teams You'll Partner with

AI Research

The AI Research team leverages, trains, and validates powerful models for our product use cases across two core areas:

• Audio/Video Research : Models for understanding, augmenting, and generating audio/video content (transcription, lipsync, video regenerate, TTS, avatars, etc.).
• LLM Research : Evaluating and optimizing LLMs for Descript products, co-designing agent architecture, experimenting with token optimizations and fine-tuning.

AI Enablement

The AI Enablement team supports integrating 1P and 3P models into the Descript product:

• Building and maintaining standardized 3P model integrations (LLM providers, generative model APIs).
• Productionizing 1P models for specific use-cases.
• MLOps infrastructure (evals framework, inference infra, training infra, data pipelines).

What You'll Do

Strategic Prioritization

• Make build vs. buy decisions : Evaluate when to train our own models vs. integrate third-party solutions based on market gaps, competitive advantage, and ROI
• Balance research investment : Allocate team resources between long-term research bets, feature work, and maintenance
• Guide research direction : Use product insight to inform what the team trains and develops; use research understanding to guide product direction

Evals & Quality

• Own the evals strategy : Design evaluation frameworks that are productionized and tied to real user needs, not just academic metrics
• Drive quality standards : Establish quality bars for 1P and 3P models