The role

Pacaso buyers are high-net-worth individuals making a considered purchase over months, sometimes years. They move between our site, email, messaging, and our Sales team, and today each of those runs on its own tools with its own picture of the customer. We believe one personalized system that follows each customer from first interest to purchase, and keeps serving them as an owner, will shorten the path to purchase and make our growth product-led.

We're hiring a principal-level technical product IC to architect that system and own its outcome. You'll define the reference architecture: how we recognize a customer across channels, what we know about them, how the system decides what to do next, and which of those decisions AI agents can make without a person. You'll prove the design with working prototypes you build yourself with AI tooling, then lead Engineering and Data in taking it to production.

This isn't a roadmap-and-backlog PM role, and it isn't a full-time production engineering role. It sits between them. You'll make product calls with Sales, Marketing, and leadership, make architecture calls with senior engineers, and be accountable for both.

Some of this is already moving. AI agents talk to our customers today, and parts of the data and decisioning exist. None of it runs end to end on its own yet. You'll take what's there, decide what to keep, and design the system that can.

What you'll own

• The architecture of the customer system. One design for new leads, dormant leads, and stalled buyers first, then owners who add a share, so the same person is served by one system instead of separate tools for each stage. You write the architecture and the decisions behind it, and other teams build against it.
• The customer intelligence layer. Identity across channels, the event and signal model, shared memory and context, scoring, eligibility and frequency caps, and the next-best-action logic that decides what happens next.
• Agent and workflow design. Which agents exist, what each one is allowed to decide, how they hand off to each other and to Sales, and the prompts, tools, and orchestration behind them.
• Trust and reliability. Evaluation frameworks, guardrails, observability, cost and latency budgets, and kill switches, in place before anything reaches a customer.
• The path to autonomy. What the system decides on its own, what still needs a person, and the evidence we need before we take that person out of the loop.
• Technical direction across teams. Build-versus-buy calls, data contracts with Engineering and Data, and integration standards for the CRM, messaging, email, and the site.
• The outcome. Funnel velocity (time from first interest to high intent, and to purchase) and the measured lift in qualified actions and closed sales.

Two examples of the work:

• An AI agent engages a new lead in the channel they prefer, qualifies them, and hands them to Sales at the right moment with the context Sales needs to close.
• A customer reads an email, chats with us, and comes back to the site a week later. They get one continuous experience, because the system remembers what we've already said and done.

Who you are

• Customer-obsessed. You start from the customer's experience, not the org chart or the backlog. When you have to choose between channels, surfaces, or features, the tiebreaker is what improves that experience. Our customers value clarity and discretion, and you design for both.
• An architect who still builds. You've designed systems other teams build on, and you still prototype yourself. You're expert at getting AI to do real work through prompting, tool use, and orchestration.
• Technically deep. You can read a data model, challenge an integration design, reason about latency, cost, and failure modes, and hold your own with senior engineers on how something should be built.
• Rigorous about autonomy. You design systems to run on their own, and you know how to prove they're ready. Evals and guardrails go in before launch, not after an incident.
• Accountable for outcomes. You measure yourself on results, not tasks shipped. You've stopped your own work when the impact wasn't there, and you can tell us about it.
• Fluent in business and technical. You can frame and defend a tradeoff with Sales, Marketing, and leadership, then go deep with engineers on the build. You own the decision in both conversations.
• A multiplier. You raise the bar for how Product, Engineering, and Data build with AI, through your designs, your reviews, and the patterns you leave behind.
• A partner. Marketing and Sales are core stakeholders, and you'll co-own the buyer journey with our product owner for new-lead acquisition. When the path to impact runs through another team's area, you work with the owner to move it.
• Fast at context. You go deep on systems you didn't build and turn a lot of information into a plan quickly.

What you'll bring

We'll ask for specific evidence of each of these. If you can't point to all five, this probably isn't the right fit yet.

1. Customer obsession. You take customer insights and translate them into real product features that deliver results.  
2. A production agentic system. You've designed and shipped an autonomous or agentic system that runs end to end with minimal human intervention. You can show us what it decides on its own and how you knew it was making the right calls before you trusted it with real customers.
3. A measurable growth outcome. You've owned a growth, conversion, lifecycle, or re-engagement result. You can give us the baseline, what you changed, the lift, the time frame, and how you knew your work caused it.
4. Decisioning and intelligence layers. You've designed signals, identity, scoring, context, eligibility and caps, or next-best-action logic.
5. Prototype to production. You've built working prototypes yourself with AI tooling, beyond specs and slides, and taken at least one to production with engineers. You can tell us what you owned and what they owned.
6. Architecture others built on. You've been the architect of a system that multiple teams built on or integrated with, and you made the core technical calls: data model, integration pattern, build versus buy.

Nice to have:

• Experience with long-consideration, high-value purchases such as real estate, luxury, or financial services.
• Experience putting AI into a human sales team's workflow, including CRM integration.
• An earlier career as a software engineer, data architect, or solutions architect.

This role isn't for you if

• You want to manage a roadmap and backlog, or execute against a defined spec.
• You want to manage a team right now. This is a principal IC role.
• You'd rather be a production software engineer full time.
• You see this as an email or campaign program, or measure success in opens, clicks, or campaigns shipped.
• Your AI experience is mostly using it to write docs and emails faster.
• You need a full team and a clean data stack before you start. Some pieces exist today and don't talk to each other yet.

How we work

We start with AI. It's one of our core values. We design processes with automation and intelligence in mind from the start, and our product team already has AI agents talking to customers.

Small team, real ownership. Product at Pacaso works closely with Engineering, Design, Data, Sales, and Marketing. If you see a problem worth solving, you're trusted to solve it. We ship weekly and demo working software, not slides.

We move fast and play the long game. We make decisions and change course quickly. "Embrace an infinite mindset" is one of our values: a quarterly number is information, not a verdict, and we'd rather send nothing than send the wrong thing.

We pick up the phone and empty the dishwasher. When async isn't enough, we get on a call. No task is beneath anyone, and we keep bureaucracy to a minimum. We're a distributed Crew.

Compensation

•  Around $220,000 with equity