Associate Product Manager, Technical (Los Altos)

Cheiron builds AI-native software for drug programs and is expanding into pharmaceutical Chemistry, Manufacturing, and Controls (CMC) teams. The Associate Product Manager, Technical will translate product concepts into engineering-ready specifications, define technical requirements for AI-driven features, collaborate with product, engineering, and life sciences teams, and own product quality for shipped features.

What you'll do:

• Break a product brief into its components, define relationships and boundaries with the product team and SMEs, and scope what goes into the build
• Write numbered feature stories with acceptance criteria, edge cases, and state transitions that an engineer can pick up cold
• Review data models and API contracts against the existing schema; identify what to extend, what to refactor, and what to leave alone
• Run specs through review with engineering and the life sciences team before handoff; resolve ambiguity during build rather than letting it travel
• Connect proactively with the life sciences team for domain input and approval on regulatory content; know when to engage them and when to move independently
• Define what “correct” looks like for AI-driven features: document extraction, regulatory classification, compliance state derivation
• Own product quality for shipped features

What they're looking for:

• 0–3 years of experience in product management, technical program management, software engineering, or a related technical role (internships count)
• Strong technical foundation: you can read a codebase, reason about system architecture, and ground a spec in what already exists. CS, engineering, or equivalent technical degree from a top-tier institution
• You have written structured technical documents: specs, design docs, architecture proposals, or research papers with clear requirements and edge cases. Academic and internship work counts
• You have built something in an environment with real constraints: a startup internship, a research lab, a side project with users, or an entrepreneurial venture
• Fluent with AI tools like Claude Code, Cursor, or equivalent. You use them as a natural part of how you work
• Clear, precise writing. A stranger reading your spec can implement it without asking you questions
• When you encounter an unfamiliar domain or system, your instinct is to build a mental model of how it works before deciding what to change
• You go deep rather than wide, and you think about your outputs from the perspective of everyone who will read them
• You have built AI-native products or projects: LLM integrations, structured extraction, agentic workflows, evaluation harnesses, or knowledge graphs
• You have a technical background (CS, engineering, math, physics) and have written production code, built infrastructure, or shipped a technical project end-to-end
• You have taken a complex domain you didn't know and built something in it. You can describe how you learned the domain well enough to make real decisions
• You have interned at or worked in a startup (under 50 employees) where you had real ownership over a product area