Product Manager, AI Models

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

• Can articulate a multi-year vision while executing on near-term priorities
• 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

What Sets Apart Great Candidates

• Understand research team dynamics
• Deeply curious about AI
• Comfortable with infrastructure work
• Balance user empathy with technical constraints

Compensation

Base salary range: $171,000 - $235,000/year