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Valency builds infrastructure for AI-accelerated science, with Bond as its flagship MCP-first platform used by national labs and enterprise research teams to ground AI systems in verified, current scientific literature. You'll own end-to-end product strategy and execution across Bond and new products in development, translating deep technical capability—LLM grounding, retrieval, the MCP protocol, corpus scoring—into a roadmap the market can understand and buy.
Key responsibilities include:
- Own the end-to-end roadmap across Bond and new products, balancing enterprise/national-lab requirements against open-platform and product-led growth models
- Translate technical architecture (MCP-based grounding, corpus ingestion, relevance scoring) into clear specs in direct partnership with engineering
- Work with GTM on segmentation and ICP, converting prospect and customer conversations into prioritized requirements
- Resolve open product risks including quality control and trust safeguards for open-access products
- Represent product in design-partner and customer conversations, turning feedback into roadmap decisions
- Help establish the product function itself—process, documentation, and team building—as one of Valency's first PM hires
You'll work daily with senior management and engineering on architecture trade-offs and with GTM on customer needs, serving as the bridge between technical capability and market demand. The role requires genuine fluency in AI and real interest in science and data, not just PM process.
Requirements:
- 4–10+ years in product management with direct experience shipping technical, infrastructure, or developer-facing products
- Fluency in AI: understand how modern LLM systems are built and where they break (grounding, retrieval, hallucination, evaluation) well enough to make roadmap calls
- Genuine interest in science and data; understand how research is produced, cited, and trusted
- Comfort operating at the protocol/API level (MCP, REST, data pipelines) and translating that into roadmap and customer value
- Excellent written communication (specs, PRDs, customer-facing narrative)
- Strong technical fluency: able to read and debate architecture with engineers
- Comfortable with ambiguity and startup pace
Preferred qualifications:
- Experience supporting enterprise or public-sector buyers (national labs, government, regulated environments)
- Familiarity with academic publishing or scholarly infrastructure (arXiv, PubMed, citation graphs, peer review)
- Experience with LLM-based products, retrieval-augmented generation, or AI evaluation systems
- Background in scientific research or academia, or experience building for researchers as end users
- Experience with two-sided platform dynamics (marketplace growth, user-generated content quality control)
The company is open to mid-level or director-level candidates; scope and title will flex to match experience.
