Product Manager, Technical (Los Altos)

Onsite, Los Altos, CA · Product · Full time · 3–6 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 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.

You'll work directly with the founders, product/design, life sciences, and engineering teams that are to create impactful products for our customers.

• 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 you spec goes directly to pharma teams making real regulatory decisions

• 3–6 years in product management or technical program management at the individual-contributor level

• You have written build-ready technical specifications (PRDs with data models, API shapes, state diagrams, acceptance criteria) that engineering built from directly

• You have shipped at a startup or mid-size company where you had fewer resources and more ambiguity

• You can read a codebase, reason about system architecture, and ground a spec in what already exists. You are not writing production code daily, but you understand how production systems are built

• Fluent with AI tools like Claude Code, Cursor, or equivalent

• 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: engineering, life sciences, etc.

• You have built AI-native products: LLM integrations, structured extraction, agentic workflows, evaluation harnesses, guardrail systems, or knowledge graphs

• You have taken a complex domain you didn't know (legal, financial, clinical, regulatory) and built products in it. You can describe how you learned the domain well enough to make product decisions beyond sourcing requirements from experts

• You have experience with document-heavy or data-quality-heavy products: quality systems, regulatory submissions, clinical data, or supply-chain traceability

• You have defined evaluation frameworks for ML or AI features: what "correct" means, how to measure it, how to build human-in-the-loop review into the product