Principal Technical Product Manager, AI Growth Systems

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 manager to own that system and its outcome. You'll own the system-level design: the components, how they fit together, what the system decides and why, and how the AI agents behave. You'll own the roadmap, and you'll do hands-on work yourself: working prototypes built with AI tooling, UI design, and the prompts behind the agents. Engineering owns the technical design and implementation, and you shape and review it with them as it goes to production.

This is a technical product leadership role, not a backlog-management role and not a production engineering role. You'll make product calls with Sales, Marketing, and leadership, work through system tradeoffs with senior engineers, and be accountable for the outcome.

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.

• The system-level design. 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 own the components, how they fit together, what the system decides and why, and the agent behavior and prompts. Engineering owns the technical design and implementation, and you shape and review it with them.
• The customer intelligence layer. What the system knows about each customer across channels and how it acts on it: signals, 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.
• The roadmap. What gets built in what order, and why. You set priorities across the site, email, messaging, the CRM, and Sales handoffs, and work with Engineering on build-versus-buy decisions.
• 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.

• 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.
• A systems thinker who still builds. You think at the system level, and you still do the hands-on work: prototypes, UI design, and prompts. You're expert at getting AI to do real work through prompting, tool use, and orchestration.
• Technically deep. You can read a data model, question an integration design, reason about latency, cost, and failure modes, and be a credible partner to senior engineers as they make design decisions.
• 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 product and system decisions, and you can follow and shape the technical ones.
• 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.

We'll ask for specific evidence of each of these. If you can't point to all six, 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. Cross-team system design. You've owned the system-level design for a product that multiple teams built on or integrated with, and shaped the technical design with the engineers who owned it.

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.

• You want to execute against a defined spec, or manage a backlog someone else shaped.
• You want to manage a team right now. This is a principal IC role.
• You want to own the technical architecture and implementation. Engineering owns those here.
• 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.

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.

•  Around $200,000- $225,000k depending on location and experience with equity (RSU's)