Staff Software Engineer, Guest Lifecycle & Loyalty
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Role details
Tech stack
Job description
- 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 todayās 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 canāt 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, and the data quality that great personalization depends on.
- Turn one-off campaign work into a durable platform, so launching a new guest journey becomes configuration rather than a bespoke engineering project - creating leverage for the entire team.
- Amplify the engineers around you - set the technical bar, mentor and grow engineers within and beyond LILO, lead design reviews, and leave the people and systems you touch meaningfully better.
- Help shape the roadmap alongside Product, Data, and Marketing, bringing clarity to ambiguous, open-ended problems and helping decide what we take on next.
Requirements
Do you have experience in Team development?, * Staff-level technical leadership. A track record of owning and driving ambitious, multi-person initiatives that cross team boundaries - from a vague but exciting idea through to measurable, durable impact. (Typically 8+ years of relevant experience.)
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A multiplier mindset. You make the engineers around you better - through mentorship, high standards, thoughtful design review, and the patterns and systems you leave behind.
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Strong backend and systems depth. Comfort designing reliable, high-scale services and data pipelines, with good instincts for where to invest in robustness and where to move fast.
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A data- and experimentation-driven mindset. You reach for holdouts, incrementality, and measurement to know whatās actually working - and youāre excited to apply ML and AI to real product problems.
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Comfort with ambiguity and a bias for impact. You turn open-ended, aspirational goals into concrete technical bets, and youāre energized rather than daunted by problems nobody has solved yet.
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Clear communication and influence. You align stakeholders and move decisions across teams without relying on authority.
Benefits & conditions
Pulled from the full job description
- Health insurance
- Unlimited paid time off, The estimated starting base salary range for this role is $220K-$240K depending on level, location and experience, plus a generous pre-IPO equity package
- Other benefits include comprehensive health coverage, work from anywhere (remote-first workplace), unlimited PTO - plus extra fun perks!
About the company
Owner is the AI-native system local business owners use to succeed, starting with restaurants.
Weāre building the system that replaces the many tools owners use to run their business.
It powers everything from the restaurantās 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
Weāre starting by helping independent restaurants succeed online.
But itās 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 - weāll 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, weāve 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, weāve helped over 20,000 restaurant owners, and saved them nearly $200 million in fees.
Our team
Our team is now in the low hundreds. Weāve got top talent from the most successful companies in SMB software, including: Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe.
Weāll 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: weāre 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 weāre 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 restaurantās 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 havenāt built yet.
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