Technical Product Management Intern (Los Altos)

Cheiron•Los Altos, CA

1d•Onsite

About The Position

Cheiron is building the first AI-native operating system designed to represent an entire drug program as a single connected system. The platform helps biopharma teams represent, reason over, and stress-test the claims, evidence, decisions, and commitments driving the drug program to completion. We are looking for a Technical Product Management Intern to turn product concepts into engineering-ready specs, working across product, life sciences, and engineering. Concepts arrive with the “what” and “why” framed. You own the “how, specifically”: how each component fits the existing architecture, what to extend, what to leave alone, and what the build looks like on paper before a line of code is written. CMC (Chemistry, Manufacturing, and Controls) is a specialized domain with its own regulatory frameworks and workflows (e.g. post-approval changes). You do not need to know it coming in. You will have a life sciences team alongside you who own the domain vocabulary and regulatory rules, and you will learn it by working closely with them.

Requirements

A strong technical foundation: you can read a codebase, reason about system architecture, and ground a spec in what already exists.
CS, engineering, or a related technical degree.
Experience producing structured technical documents that someone else could build from: design docs, specs, architecture proposals, research papers, or detailed project writeups.
Clear personal ownership of the work: you can explain what you defined, the decisions you made about scope and tradeoffs, and what you would improve.
Fluency with AI tools like Claude Code, Cursor, or equivalent. You use them as a natural part of how you work.
Initiative, sound judgment, and comfort working through ambiguous problems in a fast-moving environment.

Nice To Haves

Experience with healthcare, life sciences, regulated industries, or AI-native products is a plus but not required.

Responsibilities

Break product briefs into components, define relationships and boundaries with the product team and domain experts, 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.
Define what “correct” looks like for AI-driven features: document extraction, regulatory classification, compliance state derivation.
Run specs through review with engineering and the life sciences team before handoff; resolve ambiguity during build rather than letting it travel.
Work directly with founders, engineers, and domain experts, demonstrate progress frequently, and make pragmatic scope decisions.