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# Senior Software Engineer, Guest Lifecycle & Loyalty

- **Company:** OWNER
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $190,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Software Debugging, Software Engineering, Large Language Models, Data Pipelines
- **Published:** June 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=672e86c85f7994fa

## About the Role

Do you have experience in Software engineering?, * Strong technical leadership and action bias.
Proven ability to drive multi-quarter, cross-functional initiatives from problem framing through measurable outcomes.

 * AI-leaning builder.
Hands-on experience shipping AI-assisted features or platform capabilities (LLMs, embeddings, ranking, or ML-adjacent systems), and a pragmatic approach to evaluation, safety, and iteration.

 * Strong end-to-end ownership.
Ability to take projects from vague problem statements through design, implementation, and production support.

 * Product-oriented engineering judgment.
Able to make tradeoffs that balance speed, quality, and long-term platform leverage in a fast-moving environment.

 * Strong communication and collaboration.
Ability to align stakeholders and influence decisions across teams without authority.

 * Data & experimentation rigor.
Comfort defining success metrics, running controlled experiments/holdouts, and debugging performance changes across complex funnels.

## Description

* Own high-impact lifecycle initiatives end-to-end (from ambiguous goal design build launch iteration), spanning guest journeys like activation, reactivation, graduation, and loyalty.
 * Build reliable messaging and data pipelines that power campaign execution (segmentation, scheduling, send-time optimization) across email/SMS/push at scale.
 * Raise the bar on deliverability and compliance (domain warmup, provider policies, Twilio registration, throttling/guardrails), ensuring we can send safely while maximizing reach and performance.
 * Establish an experimentation + measurement loop for lifecycle: holdouts, incremental lift, attribution quality, and analytics instrumentation that product and marketing can trust.
 * Improve platform observability and operational excellence with SLOs, dashboards, alerting, and incident-ready runbooks for business-critical pipelines.
 * Ship AI-assisted capabilities that make lifecycle marketing more autonomous (e.g., smarter targeting, creative generation, and adaptive campaign optimization), with a bias toward production-ready systems.
 * Multiply the team through technical leadership: set engineering standards, review designs, mentor engineers, and partner cross-functionally with Product, Data, Marketing, and other GPV Growth teams.


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