Technical Product Management Intern
Cheiron
Technical Product Management Intern
Cheiron
AI-powered OS for drug programs
Los Altos, CA · Product · Internship · Semester/quarter or summer · Fully in-person
Cheiron raised an $11 million seed round led by Menlo Ventures, bringing total funding to $16 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.
We are looking for a Technical Product Management Intern to solve product problems end to end. You go deep into how CMC and regulatory teams actually work and how they use our product: how a manufacturing change moves from the plant through QA and regulatory affairs to a filing in each market, who decides what, and where today’s tools fail them. You keep going until you understand the problem better than the brief that named it, then turn it into real user workflows, engineering-ready specs, and data models, settled 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 learn it first-hand, from the regulations and guidance themselves, from customers’ own trackers and filings, and from the people who do the work, with a life sciences team alongside you who own the regulatory rules and check your reasoning. What matters is that you’re the kind of person who immerses until you can reason from first principles.
Go deep on how our users work: talk to customers, read the trackers, SOPs, and filings they work from, and map each workflow step by step across regulatory affairs, QA, and manufacturing before deciding what to build.
Know what customers’ existing systems, such as RIM and QMS, already cover and where they stop, and decide what Cheiron adds on top, including when the honest answer is nothing.
Turn what you learn into real user workflows (who does each step, what they need to see, what they decide), then break them 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.
Design data models and API contracts against the existing schema; identify what to extend, what to refactor, and what to leave alone.
Prototype the workflows you spec, so customers and engineers react to working screens rather than a document.
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.
Use the live product the way our users do, and fix what gets in their way.
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.
You have found the real problem yourself: you went to the users, learned how the work actually gets done, and changed what got built because of it. Class, research, internship, and project work count.
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 to go deeper, not to skip the thinking.
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.
$35–$55 per hour. Exact compensation within the range depends on demonstrated expertise, relevant experience, and the time the student can commit.
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.
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.
Apply at https://jobs.ashbyhq.com/cheiron/e193c411-f175-480e-a341-72e7131e009a
We are also hiring a Product Manager, Technical (3–6 years) at https://jobs.ashbyhq.com/cheiron/7b17db14-aeb6-4dbe-a731-990e5a6c7fda and an Associate Product Manager, Technical (0–3 years) at https://jobs.ashbyhq.com/cheiron/a8514f9b-17d9-40a0-8674-a07543bb86d0
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.
Cheiron builds an AI-native operating system for drug programs, treating therapy work as a connected system rather than scattered documents. Its core is the Life Sciences Knowledge Graph, linking biomedical, clinical, regulatory, patent, and commercial data to enable inferences such as how trial design aligns with regulatory precedent or contradictions between competitor data and patents. The product organizes live data into a graph-based model that represents claims, evidence, assumptions, risks, decisions, and commitments to support clinical, regulatory, and strategic workflows. Cheiron differentiates itself with a live knowledge-graph backbone that unifies information across domains, aiming to speed and improve drug development by providing a continually updated, connected view to guide design and strategy.
Explore more opportunities at Cheiron