Descript
Product Manager, AI Research
1mo ago
USAaimachine learningproduct strategy
Lead AI Research and Enablement roadmap to build future video editing products powered by AI.
Requirements
- 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
Other
- 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.
- 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.
- 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).
- 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
- 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
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
- 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
- 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
- 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 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 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 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
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- Impact : Your decisions will directly shape Descript's AI strategy and competitive positioning
- Scope : You'll own both research direction and production infrastructure — a rare combination
- Team : Work with world-class researchers and engineers pushing the boundaries of AI for creativity