Product Manager, AI Research

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 Designs experiments and A/B tests to validate hypotheses Comfortable with SQL, experimentation platforms, and analytics tools Communication & Documentation Writes clear decision documents with explicit tradeoffs, pros/cons, and alignment dates Can explain complex technical concepts to non-technical stakeholders Proactively shares context and builds alignment across teams Operational Excellence Creates processes and frameworks to help teams move faster (not slower) Establishes clear DRIs, success metrics, and timelines Balances speed with quality and manages risk appropriately What Sets Apart Great Candidates You have strong opinions, loosely held You can make confident recommendations based on available data, but you're open to changing your mind when new information emerges You engage in healthy debate and welcome pushback from your braintrust You understand research team dynamics You know how to motivate researchers and give them space to explore w