At Mondrio, we’re on a mission to build the future of pricing: agentic pricing management. We believe that monetization should be data-driven rather than a one-time exercise. It should evolve as products, markets, or sales motions change. Our AI-native platform equips companies to bring that pricing intelligence into strategy and deal decisions.

About Mondrio
Mondrio is a fast-growing Seed stage startup built on deep pricing experience from our founding team who have run over 150 pricing engagements. We’ve seen firsthand how companies struggle with pricing and leave money on the table. On the flipside, we’ve also seen firsthand how companies that make pricing a continuous capability thrive.

We have assembled our team of 8 (pricing experts and engineers) to deliver that capability. Our main hubs are San Francisco and Amsterdam, covering the North American and European markets. We believe owning those markets are key to building a category-defining company.

We're backed by renowned investors who've helped build generational companies, and we expect to grow the team 2-3x in the coming 12 months. Joining now means you’ll actively shape the product, the team, and the way we run engagements. If that kind of ownership and entrepreneurship sounds energizing rather than daunting, then this role is for you.

About the role

We are looking for strong engineers to be part of the next stage of Mondrio’s growth and set the bar for our engineering and technical culture.

You will be at the forefront of building the next frontier of pricing and monetization software as the industry repackages and reprices far more frequently in the agentic era.

You own whole modules of our growing product suite end to end: scope with customers, design the API, build the UI, ship, iterate. A module is done when an LLM agent can drive it through our MCP server for consumption with conversational interfaces rather just when the React component renders.

You’ll mentor peers, influence product strategy and help us define what best-in-class AI-driven pricing software looks like.

No PM sits between you and the customer. You will be the direct bridge between the product vision of the executive team, shaped by customer feedback and the vast new engineering capabilities available in the agentic era.

What you'll do

• Partner closely with product, design, and leadership to define and evolve the architecture for agentic AI systems that across multiple product surfaces and interfaces (Slack bots, TUIs, Web, vendor integrations).
• Own the design and delivery of our entire product suite: scope with customers, design the versioned api and MCP endpoints, build the React UI, ship, iterate.
• Lead technical discussions and architectural reviews, ensuring our systems remain robust, extensible, and secure.
• Build backend-heavy product areas. Current examples: pricing simulation tooling, AI persona modeling, and the voice-of-customer survey module.
• Move pricing rules out of the client and onto the server. The frontend is meant to be a thin, replaceable layer, and some pricing logic still lives in the React client.
• Make every feature drivable by an LLM agent through our MCP server.
• Extend our typed ontology of pricing entities (Pydantic models for SKU, Proposition, Persona, and Pricing today; Customer, Contract, and Quote next).
• Establish engineering best practices around testing, observability, deployment, and performance monitoring for AI-driven features.
• Treat money handling as load-bearing since it has massive repercussions for financial fidelity.

Your first 90 days

First 30 Days: SDLC Baseline & Product Exploration

• At least one set of client-side pricing rules is migrated out of the React UI and onto the FastAPI backend, and the redundant frontend code is deleted.
• You adopt our AI-native SDLC using Claude Code and Cursor, helping configure the initial automated review gates for agent-assisted PRs.
• You establish a weekly rhythm of demoing real, un-staged application state and join direct customer discovery conversations.

By Day 60: End-to-End Module Ownership & Agentic Parity

• You own a complete module within our product suite end-to-end from scoping customer problems directly to designing versioned API endpoints and building advanced React components.
• You mentor peers on our architectural guidelines, ensuring business logic stays server-side, APIs evolve additively, and writes are audited by default.
• Every feature within your module is exposed through our FastMCP server, making it fully drivable by an LLM agent via conversational interfaces.
• You refine our software factory workflows so LLM agents reliably write and review pull requests behind our automated review gates.

By Day 90: Production Impact & Ontology Expansion

• You own the operational architecture of our AI-native software factory, turning recurring engineering tasks into automated agentic pipelines that maintain absolute financial fidelity.
• You mentor incoming engineers and set the bar for engineering best practices around testing, observability, and performance as the team scales.
• You can live-demo an agent driving your shipped module end-to-end in production and directly translate customer interactions into shipped code without a PM layer.

What we're looking for

• 8+ years of engineering experience, with strong skills working across the product stack.
• You have shipped and owned entire product areas at a startup, at staff-level scope.
• Strong backend depth. We use FastAPI, Python, and MongoDB, and deep experience in a comparable stack counts.
• Credible frontend range with React and TypeScript. You can ship a clean UI on your own.
• You can take an ambiguous customer problem to a shipped feature without a PM, and you have done it before.
• Interest in monetization and the mechanics of B2B SaaS: pricing models, packaging, quoting.
• You can show how AI coding tools fit into your work today. We weigh that over where you studied or previous role.
• You have high standards for core logic. Moving fast is essential, but when handling money and financial fidelity, precision matters.
• You default to root-cause analysis when things break rather than applying quick patches.
• Work authorization: You must be authorized to work in the US. We're unable to sponsor visas at this time.

Nice to have:

• You have built MCP servers or other tooling for LLM agents.
• Experience with billing, CPQ, metering, or pricing systems.
• Experience building data-dense frontend components and/or conversational interfaces
• Work in a domain where correctness is audited, such as pricing, billing, or payments.
• Familiarity with data residency or compliance constraints. SOC2 and GDPR shape what you build against.
• Previous experience as a technical founder, co-founder, or employee #1–5 who shipped a 0-to-1 B2B product to paying customers.

Our stack

Client: Typescript, React, Vercel Chat SDK

Server: Python, FastAPI

Data: Mongo, Atlas

Infra: GCP, Pulumi, Cloudflare Pages

AI: FastMCP, Langfuse, Claude Code, Cursor, Vercel Eve

Security: SOC2 Type 1/Type 2, GDPR compliant, EU and US data residency

How we work

We are under ten people, and everyone ships and talks to customers. Engineers are product engineers: you own outcomes, scope your own work, and demo every week.

We work in-person 5 days a week at our downtown San Francisco office. We believe the best products get built when people are in the same room. Fast feedback loops, real collaboration, and a team that genuinely enjoys working together. If that's how you do your best work, you'll fit right in.

There is no separate PM layer. Our SDLC is AI-native as we move to a robust software factory. Claude Code and Cursor are standard kit, and LLM agents write and review pull requests behind automated review gates.

We keep pull requests small, around 500 lines, because the research on small batches holds up and we act on it.

The architecture rules are short and enforced: the API is the product so it evolves additively, business logic stays server-side, and writes are audited by default for compliance. Demos show real state, with no staged data over broken features.

What we offer

• Compensation: $200,000 - $250,000
  Plus meaningful equity through our employee stock option plan (ESOP). We aim to be highly competitive on cash and generous on equity. Joining at this stage means a real stake in what we build together.
  This compensation and benefits information is based on our good faith estimate for this position as of the date of publication and may be modified in the future. Employees based outside of the US will receive a different benefits package. The level of pay within the range will depend on a variety of job-related factors, including where you place on our internal performance ladders, which is based on factors including past work experience, relevant education, and performance on our interviews or in a work trial.
• Benefits: A market-conform package, including health coverage, pension/retirement provision, generous paid time off, paid parental leave.
• Direct customer contact from early on: you hear how your recommendations land.
• Real influence on the architecture while the big decisions are still open. The AI layer is young, and you set its shape.
• A team where your impact is visible.
• An opportunity to be part of a fast-growing company.
• Daily work with, and real influence over, a cutting-edge AI pricing engine.
• Continuous exposure to the newest AI tools and techniques.

How to apply

Send a short note about a product area you shipped and owned, including what you scoped, what you cut, and what happened after launch. Add your take on how B2B pricing should work. If you have public work, link it. If your best work is private, which most is, the written walkthrough counts just as much.

Don't check every single box? Apply anyway

We set a high bar, but we know exceptional engineers rarely fit a perfect checklist. If you don’t meet 100% of the qualifications above, but you’ve shipped complex software, care deeply about quality, and are excited to solve hard problems in AI and pricing, please apply anyway. We value agency, fast execution, and engineering fundamentals far more than rigid credentials.