Job description

Los Altos, CA · Product · Internship · Semester/quarter or summer · Fully in-person COMPANY OVERVIEW In July 2026, Cheiron announced an $8 million seed round led by Menlo Ventures, bringing total funding to $13 million to date, with the backing and strategic support of industry veterans including Moderna co-founder and MIT Institute Professor Robert Langer, former Pfizer Chief Medical Officer Freda Lewis-Hall, Chai Discovery co-founder and CEO Josh Meier, former Starbucks CEO Laxman Narasimhan, and former Apple AI chief John Giannandrea. Cheiron is building the first AI-native operating system designed to represent an entire drug program as a single connected system. The company's platform helps biopharma teams represent, reason over, and stress-test the full state of a drug development program, including the claims, evidence, assumptions, risks, decisions, and commitments that determine whether a therapy advances. In less than six months since launch, Cheiron has been adopted by tens of thousands of biopharma professionals and deployed by major drug developers, and is already used by 7 of Korea's top 10 biopharma companies. Cheiron is expanding into pharmaceutical CMC (Chemistry, Manufacturing, and Controls) teams. CMC governs how a drug is made, tested, and kept consistent across its entire commercial life. The work involves hundreds of regulatory commitments, post-approval changes, and cross-market submissions. Today it runs on documents, spreadsheets, and institutional memory. Cheiron augments manual workflows with a CMC regulatory-specific intelligence and reasoning layer. Founded in 2024 by Stanford-trained AI researchers. The team includes leaders with combined decades of experience across pharma and biotech. Headquartered in Los Altos, California. We are building and deploying the product now with rapid expansion into global markets. ABOUT THE ROLE 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. WHAT YOU’LL DO - 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. WHAT WE’RE LOOKING FOR - 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. - Experience with healthcare, life sciences, regulated industries, or AI-native products is a plus but not required. COMPENSATION $35–$55 per hour. Exact compensation within the range depends on demonstrated expertise, relevant experience, and the time the student can commit. LOCATION & COMMITMENT This internship is based in our Los Altos, CA office and requires fully in-person work. Semester or quarter interns must be available for at least 20 hours per week; co-op and summer interns should be available full time. Relocation support may be available on a case-by-case basis. We consider candidates with different immigration and work-authorization circumstances where lawful employment is possible. INTERVIEW PROCESS - 45-minute technical screen focused on how you break down problems, structure requirements, and reason about architecture and tradeoffs. - One-day onsite in Los Altos with a hands-on case interview: decompose a brief, scope a build, and write a spec under realistic constraints. EQUAL OPPORTUNITY & ACCOMMODATION Cheiron is an equal-opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law.Los Altos, CA · Product · Internship · Semester/quarter or summer · Fully in-person

Requirements

Must-haves

Los Altos, CA · Product · Internship · Semester/quarter or summer · Fully in-person
We are looking for a Technical Product Management Intern to turn product concepts into engineering-ready specs, working across product, life sciences, and engineering.
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.
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.
Relocation support may be available on a case-by-case basis. We consider candidates with different immigration and work-authorization circumstances where lawful employment is possible.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapi

Platform: ashby
Effort: quick