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 while keeping them aligned to product goals

• You can translate between "research interesting" and "product valuable"

• You understand that research timelines are inherently uncertain and can navigate that ambiguity

You're deeply curious about AI

• You stay up-to-date on the latest models, techniques, and research papers

• You have a point of view on where AI is headed and what it means for creative tools

• You're excited about the opportunity to build AI-native products, not just add AI features

You're comfortable with infrastructure work

• You understand that great AI products require great infrastructure

• You see MLOps and tooling as strategic investments, not just "plumbing"

• You can get excited about improving evals frameworks or inference latency

You balance user empathy with technical constraints

• You advocate for the user experience while understanding technical and cost limitations

• You can make pragmatic tradeoffs between quality, cost, and speed

• You think about the entire user journey, not just individual features