Associate Product Manager, Technical (Los Altos)

Onsite, Los Altos, CA · Product · Full time · 0–3 years experience

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.

You will 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 is a specialized world with its own regulatory frameworks and its own workflows. You do not need to know it coming in. You’ll have a life sciences team alongside you who own the domain vocabulary and regulatory rules, and you’ll learn the domain by working closely with them. What matters is that you’re the kind of person who immerses until you can reason from first principles.

This is a ground-floor role at an early-stage company. You’ll work directly with the founders, product/design, life sciences, and engineering teams. The learning curve is steep, the scope is real, and your specs go directly to pharma teams making real regulatory decisions.

• 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

• 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

• You have experience with document-heavy, data-quality, or compliance-adjacent products