---
title: 'Associate Product Manager, Technical (Los Altos) at Cheiron'
canonical: 'https://feeny.ai/job/associate-product-manager-technical-los-altos-cheiron-los-altos-zw6f9jp121t2'
type: 'job'
last_seen: '2026-11-09'
---

# Associate Product Manager, Technical (Los Altos) at Cheiron

- **Company:** Cheiron
- **Location:** Los Altos, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-09-16
- **Last confirmed live:** 2026-11-09
- **Apply:** https://jobs.ashbyhq.com/Cheiron/a8514f9b-17d9-40a0-8674-a07543bb86d0

## Job description

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

## About Cheiron

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.

## The role

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.

## What you will do

- 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 we are looking for

- 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

What would make you stand out

- 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

## About Cheiron

## Company Overview
- **One-liner**: Cheiron is building the AI-native operating system for drug development, unifying clinical, regulatory, scientific, and competitive workflows on a single intelligence layer.
- **Entity Type**: Private (Seed-stage – $4M raised in September 2025)
- **Headquarters**: Palo Alto, California, United States (with offices in South Korea and India)
- **Founded**: 2024
- **Founders**: Minseok Bae (CEO), Harshit Gupta (Co-Founder), Jason Park (Co-Founder & CPO)

## Core Business
- **Primary industry**: AI-powered software for biopharma R&D, clinical development, regulatory affairs, medical affairs, and commercial strategy.
- **Target customers**: B2B – biopharma companies, drug developers, and life science teams (enterprise).
- **Mission/purpose**: To provide a unified evidence-to-decision platform where biopharma professionals can execute end-to-end workflows with traceable, verifiable AI outputs.

## Products & Services
- **Cheiron AI Operating System**: A SaaS platform that integrates peer-reviewed literature, clinical trial data, regulatory filings, safety data, and patents into a single Lifesciences Knowledge Graph. Workflow-native AI agents produce decision-ready outputs (competitive landscapes, regulatory comparisons, target product profiles, safety signal write-ups) that are traceable to source evidence. Supports use cases across R&D, Clinical Operations, Regulatory Affairs, Medical Affairs, CMC, Quality, Pharmacovigilance, and Commercial.

## Market Standing
- **Valuation/Market Cap**: Not disclosed.
- **Key Metric**: Total Funding – $4.0M (Seed round led by SK Networks on September 4, 2025, with three investors).
- **Notable Investors/Partners**: Lead investor SK Networks; other investors undisclosed.
- **Growth Signals**: Headcount grew 56.2% YoY to ~20 employees (as of mid-2026). LinkedIn followers surged 163.5% in the past year. Monthly website traffic grew 235% (though starting from a low base). Actively used by professionals from leading biopharma organizations (specific names not publicly listed).

## Competitive Advantages
- **Integrated Lifesciences Knowledge Graph**: Cheiron structures cross-domain evidence (literature, trials, regulations, safety, patents) into a single curated substrate, allowing AI agents to produce outputs that are grounded, traceable, and verifiable.
- **End-to-end workflow coverage**: Unlike point-solution search tools, Cheiron addresses the full span of drug program deliverables — from research and drafting to review and regulatory submission prep.
- **Evidence-linked verifiability**: Every answer is linked to its exact source passage, enabling users to pressure-test claims against authoritative evidence before submission to regulators or medical reviewers.

## Strategic Focus
- **Current priorities**: Scaling from early traction to enterprise deployments. Deepening product capabilities for clinical development, regulatory affairs, medical affairs, and commercial workflows. Hiring for key roles (Product Manager, Market Access/HÉOR Specialists, Commercial Specialists) to expand into Boston and Palo Alto.
- **Direction for growth**: Building the operating layer for drug programs across biopharma, moving beyond search into full workflow automation while maintaining traceability.

## Why Work Here
- **Culture**: Early-stage, fast-shipping environment with “comfort with ambiguity, intensity, and the rhythm” of a startup. The team values systems thinking, pragmatic product decisions, and deep pharma domain expertise.
- **Work policy**: On-site presence expected (Palo Alto & Boston) based on current job postings.
- **Notable perks/engineering culture**: Opportunity to define how AI agents reason over clinical and regulatory evidence; work directly with pharma users (clinicians, regulatory, medical affairs, commercial teams); own 0→1 product development on high-impact health-tech problems. Strong emphasis on cross-functional collaboration between domain experts and engineering.
- **Talent profile**: Team includes alumni from Stanford AI Lab, IIT Bombay, NYU Courant, and prior experience at pharma tech companies. Departments span technical (32%), operations (16%), product (8%), and more.

## Sources
1. [cheiron.bio](https://www.cheiron.bio/)
2. [LinkedIn – Cheiron](https://www.linkedin.com/company/cheiron-bio)
3. [Cheiron Framer Career Site](https://cheiron.framer.ai/)
4. [BeeBee – Cheiron Jobs](https://bebee.com/us/companies/cheiron)
5. [Jobright.ai – Product Manager Job Posting](https://jobright.ai/jobs/info/6a15e0b00073182bd92ad6db)

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