Product Manager, AI Models

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 before they ship to users Build feedback loops : Instrument data pipelines to continuously learn from user behavior and improve model performance Cross-Functional Orchestration Partner with product teams : Advise on which models or architectures are best suited for specific features over time Enable fast iteration : Build infrastructure and processes that let product teams experiment with AI capabilities quickly Manage dependencies : Coordinate research timelines with product roadmaps and feature launches Cost & Infrastructure Optimize COGS : Make strategic decisions on model selection, caching strategies, and infrastructure to balance quality, latency, and cost Scale research infrastructure : Ensure the team has the DevEx, training infra, and tooling to move fast Required Experience Product Sense 4+ years of product management experience, with at least 1-2 years working on AI/ML products Track record of making sound build vs. buy decisions in the AI space Experience balancing research exploration with shipping product value Ability to translate technical capabilities into user-facing product features Technical Foundation Understanding of modern ML/AI systems and LLMs (you don't need to write the code, but you need to understand the tradeoffs) Experience shipping AI/ML products to production at scale Experience with evals frameworks, model training pipelines, and inference infrastructure Understanding of ML cost structures (training compute, inference costs, token economics) Cross-Functional Leadership Experience working with research teams and helping them focus on high-impact work Track record of partnering with engineering teams on infrastructure and platform work Comfortable operating in ambiguity and setting direction when the path isn't clear Skills & Competencies Strategic Thinking Can articulate a multi-year vision while executing on near-term priorities Understands when to make strategic long-term bets vs. tactical short-term wins Evaluates competitive landscape and market trends to inform research direction Data-Driven Decision Making Uses data (evals, user feedback, cost metrics) to shape proposals and drive alignment