Associate Product Manager, Technical (Los Altos)

Cheiron • Los Altos, CA

1d • Onsite

About The Position

Cheiron builds AI-native software for drug programs and is expanding into pharmaceutical CMC (Chemistry, Manufacturing, and Controls) teams. CMC governs how a drug is made, tested, and kept consistent throughout its commercial life, involving numerous regulatory commitments, post-approval changes, and cross-market submissions. Currently, this process relies on documents, spreadsheets, and institutional memory. Cheiron aims to augment these manual workflows with an intelligence and reasoning layer specific to CMC Regulatory.

In this role, you will transform product concepts into engineering-ready specifications, collaborating across product, life sciences, and engineering teams. While the "what" and "why" of concepts will be provided, you will be responsible for defining the detailed "how." This includes determining how each component integrates with the existing architecture, what needs to be extended, what should remain unchanged, and outlining the build on paper before any coding begins. The CMC domain is specialized, with its own regulatory frameworks and workflows, but prior knowledge is not required. You will be supported by a life sciences team that manages the domain vocabulary and regulatory rules, and you will learn the domain through close collaboration. The key is your ability to immerse yourself in the subject matter until you can reason from first principles. This is a ground-floor opportunity at an early-stage company, offering direct work with founders, product/design, life sciences, and engineering teams. The role presents a steep learning curve, significant scope, and your specifications will directly impact pharmaceutical teams making critical regulatory decisions.

Requirements

• 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

Nice To Haves

• 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

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Entry Level