Product Management Intern, Agentic AI

Location: Hybrid - San Mateo, California Department: Parspec US Employment Type: FullTime

About Parspec

Founded in 2021, Parspec is revolutionizing the materials supply chain for the $13 trillion USD construction industry by digitizing and organizing the industry's product data. Our proprietary AI technology maintains a current and comprehensive catalogue of millions of products, enabling our customers to instantly identify the products that best meet their needs. Trusted by top designers, builders, distributors, manufacturers, and sales agents, and backed by leading venture investors, Parspec is paving the way for a more innovative, connected, and sustainable future in construction.

Join us in building transformative technology that reshapes one of the world's oldest and largest industries.

The Opportunity

What does it take to turn an AI agent into a product customers trust and pay for? That question sits at the heart of this internship.

We are looking for an intern to work alongside Parspec's product leadership on our AI quoting agent, the product at the center of how distributors and rep agencies build quotes. You will own a piece of it end to end, from customer discovery through an engineering-ready product spec, and help shape the business model behind it. Quoting is the beating heart of our customers' business. It is also messy, manual, and full of judgment calls, which is exactly what makes it both a hard and a valuable problem for an AI agent to take on.

This is not a shadowing role. You will talk to real customers, write requirements that engineers build against, and build prototypes. If you see yourself becoming an AI product leader or founding a company someday, this is a chance to work on the two problems that define AI product management right now: making agents reliable and making them commercially viable.

What You Will Achieve and Key Responsibilities

Define what to build

Interview distributor and rep agency users to understand how quotes get built today, step by step, including the workarounds and judgment calls that never show up in a process diagram

Map the quoting workflow end to end and identify where an AI agent delivers the most value, and where human judgment needs to stay in the loop

Turn what you learn into a clear, prioritized view of what the agent should do next, backed by customer evidence rather than intuition

Prototype and validate

Build working prototypes to test agent workflows with real users before engineering commits to a full build

Translate customer needs into product requirements and an engineering-ready spec for your piece of the agent

Define the success metrics that tell us the agent is actually working: accuracy, time saved, trust, and adoption

Set the release criteria the agent must meet before it reaches customers, including how we evaluate reliability on real-world quoting scenarios

Shape AI pricing

Analyze the value the agent creates for customers, how usage actually behaves, and what it costs us to serve

Study how other AI products charge for agentic work, from seats to usage to outcomes, and what those models would mean for our customers and our margins

Recommend how Parspec should charge for AI-powered work, with the analysis to back it up, and present it to leadership

Why This Matters

Every software company is racing to put AI agents in front of customers. Very few have figured out the two things that actually matter: making the agent reliable enough that customers trust it with real work, and pricing it so the business works. Parspec is live in the market with AI doing real quoting work for real customers, which means these are not thought experiments here. The quality bar you set shapes what customers experience, and the pricing model you recommend shapes how the company grows. Few internships put you that close to the decisions that matter.

Who You Are

You are curious about how products get built and sold. You like talking to users as much as you like working through data, and you can hold both the customer's reality and the business model in your head at the same time. You write clearly, you ask sharp questions, and you are comfortable forming a view on a problem nobody has fully solved yet. You are not afraid to roll up your sleeves and go deep into a messy workflow to map complex processes and identify how AI can enhance them. You do not need to be an AI engineer, but you need to be genuinely curious about how AI agents work, what makes them fail, and what makes people trust them.

Mandatory Qualifications

Currently pursuing or recently completed a degree (undergraduate or graduate) in Business, Economics, Engineering, Computer Science, Data Analytics, or a related field

Strong analytical skills, including comfort working through usage data, unit economics, and pricing trade-offs

Structured problem-solving ability: you can break an ambiguous question down into an answerable workplan

Excellent written and verbal communication skills, including the ability to run a customer interview and write a spec an engineer can build from

A real ownership mentality: you have taken something from question to shipped result without being managed through it

Genuine interest in AI products: you have used them seriously, formed opinions on what works and what does not, and can articulate why

Preferred Experience

Prior professional experience in product management, consulting, software engineering, or a startup operating role

Hands-on experience building with or evaluating LLMs and AI agents, even on personal projects

Exposure to B2B SaaS business models, pricing and packaging work, or product discovery methods

Interest in construction, building materials, industrial distribution, or vertical SaaS businesses

Experience in fast-paced or early-stage startup environments

What We Offer

Direct, sustained working exposure to Parspec's product leadership

Ownership of a live piece of an AI product that real customers use, wit