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 across its entire commercial life, involving numerous regulatory commitments and submissions. Currently, this process relies on documents, spreadsheets, and institutional knowledge. Cheiron aims to enhance 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. You will collaborate with product, life sciences, and engineering teams, taking ownership of the detailed 'how' after the 'what' and 'why' are established. This includes defining how each component integrates with the existing architecture, what needs extension, what should remain unchanged, and outlining the build on paper before development begins.

While prior knowledge of CMC is not required, you will learn the domain by working closely with a dedicated life sciences team. The ideal candidate is someone who immerses themselves in a subject until they can reason from first principles. This is a ground-floor opportunity at an early-stage company, offering direct collaboration with founders, product/design, life sciences, and engineering teams. The role involves a steep learning curve, significant scope, and the direct impact of your specifications on 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