Staff Software Engineer, Guest Lifecycle & Loyalty

About Owner

Owner is the AI-native system local business owners use to succeed, starting with restaurants.

We’re building the system that replaces the many tools owners use to run their business.

It powers everything from the restaurant’s website, online ordering, CRM, POS, and more.

Product philosophy

Most small business software makes owners do the work to get what they want: sales growth and profit growth. Owner does the work for them agentically.

Our system drives demand, converts it, and helps operators run their business day to day. As it improves, the business improves with it.

Using Owner should feel like having a team of great operators, engineers, and marketers working for you.

Our vision

We’re starting by helping independent restaurants succeed online.

But it’s not just restaurants that need our help. Most local businesses are struggling with these same problems. Huge technology corporations are taking their customers, bleeding their profits, and making it hard for them to survive.

Once we nail the solution for restaurants – we’ll scale it into every other local business type.

In the future we envision, tens of millions of local business owners will use our technology to succeed in the digital age.

Read our Series C memo here →

Our traction

Since 2020, we've generated tens of millions in revenue and processed over a billion dollars of online orders. 1 in 5 Americans have used an Owner.com website.

More importantly, we’ve helped over 20,000 restaurant owners, and saved them nearly $200 million in fees.

Our team

Our team is now in the low hundreds. We’ve got top talent from the most successful companies in SMB software, including: Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe.

We’ll be scaling even faster in 2026 to keep pace with our customer growth.

Where we work

Owner is a remote-first, global company headquartered in San Francisco, with a sales hub in Toronto. For a few of our roles we prioritize in-person collaboration at one of our office locations. Most of our teammates are distributed throughout the globe. Please review the role description and discuss with your recruiter for more details on location!

About the Team:

The Guest Lifecycle & Loyalty (LILO) team is building what amounts to a world-class marketing agency for every restaurant on Owner — running automatically, at scale, with data-driven optimization that even a great agency could never match. We own the messaging, loyalty, and intelligence that turn first-time guests into repeat guests and long-term regulars across email, SMS, and push, along with the systems that power segmentation, experimentation, and measurement — so restaurants can reliably grow repeat orders and lifetime value.

Our work sits at the intersection of product, data, and deliverability/reliability: we're scaling high-volume communications safely, improving attribution and experimentation rigor, and raising the quality and timeliness of the customer data that drives personalized outreach.

And we're at an inflection point. The foundations — reliable delivery, trustworthy attribution, modern campaign infrastructure — are coming together, and that unlocks the exciting part: making marketing radically more personalized at both the restaurant and the guest level. Picture campaigns that generate themselves from real-world signals, creative that adapts to each restaurant's brand voice, and channel, item, and offer decisions optimized for each individual guest. Some of that will run on LLMs; much of it, at guest scale, will require classic ML systems we haven't built yet.

What you'll work on:

• Set the technical direction for the personalization platform — the systems that decide who we reach, when, on which channel, and with what creative, item, and offer — and chart the path from today's foundation toward an increasingly autonomous, agent-assisted future.

• Build the intelligence layer, combining LLM-powered and classic ML approaches to personalize marketing at restaurant and guest scale, where cost and latency mean we can't just reach for a model call every time.

• Own the hard, high-leverage problems that make all of this possible and trustworthy — reliable high-volume sending, an experimentation and measurement engine teams can trust, an