Senior Software Engineer II, Mobile - Revenue Experimentation (AI Native)

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with...

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basica...

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Design and build high-quality Android growth and monetization features and experiments — subscription paywalls and pricing tests, ad placement and mediation experiments, one-time purchase (IAP) flows, and upsell/upgrade surfaces — running dozens of concurrent tests across the Life360 app.
Partner with product, design, data science, and analytics to translate hypotheses into shippable experiments, and turn results into the next decision — pushing back when a hypothesis or plan doesn't hold up.
Work with AI (Claude Code or equivalent) as a first-class collaborator — your primary workflow involves orchestrating agents to create specs, generate code and tests, verify results, and perform reviews.
Instrument experiments accurately — exposure logging and analytics — so the results downstream teams rely on are trustworthy.
Ship polished, performant, reliable Android experiences to tens of millions of users, with strong attention to startup time, memory, battery, ANR, and crash-free rates.
Take ambiguous or partially-specced ideas and get a working version in front of real users fast — treating the first cut as a learning tool, not a final answer.
Help define and codify AI-native Android engineering practices for the team, establishing playbooks the broader mobile org can adopt.
Contribute to architectural decisions, code reviews, and a culture of craft and continuous improvement on the Android codebase.
Participate in on-call rotation and incident response for mobile experiments and releases.
Mentor other engineers and contribute to team processes, technical standards, and the evolution of the team's AI-native engineering practices.

6+ years of Android software engineering experience shipping production apps at consumer scale.
At least 1 year of hands-on experience prompting, evaluating, and building with LLMs.
Strong proficiency with Kotlin and Jetpack Compose (and the Android View system); deep familiarity with the modern Android toolchain (Android Studio, Kotlin Coroutines, Flow).
Experience with Android architecture patterns (MVVM, MVI, Clean Architecture, Jetpack/ViewModel/Navigation, Hilt or Dagger) in a large, multi-team codebase.
Comfort with mobile experimentation concepts: feature flagging, A/B testing, exposure logging, holdouts, and guardrail metrics.
Solid grounding in mobile performance, memory, battery, ANR, and crash diagnostics (Android Studio Profiler, Perfetto/Systrace, Macrobenchmark, Crashlytics or equivalent).
Experience with CI/CD for Android (Gradle, Fastlane, GitHub Actions, Bitrise, or similar) and modular build systems.
Deep experience with agentic workflows, prompt engineering, context-window management, and MCP/function calling — you understand the research-plan-implement cycle but don't outsource thinking to agents.
Bachelor's degree in a technical field, or equivalent experience.
Agency — You don't wait to be told what to do. Given a rough hypothesis or a half-finished doc, you can get a working version in front of users without needing a fully-specced ticket first, and you take responsibility for the outcome, not just the task.
Ownership mentality — You own your work from design through production and beyond, including what happens when the experiment doesn't go as planned.
Taste and craft — You have opinions about what a good experiment, a good rollout, and a good user experience look like, and you can articulate why — not just execute a spec.
Growth mindset — You structure problems precisely, learn quickly, show interest outside "your lane," and are always happy to try something new.
AI-native working style — You use AI tooling (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams rather than hand-holding one agent at a time.
Collaborative approach — You communicate clearly, work well across teams, and value diverse perspectives; you give and take feedback well.
We believe culture fit and problem-solving ability matter more than checking every technical box. We're happy to help you grow into areas where you have less experience.
Experience working with an experimentation platform (Statsig, LaunchDarkly, Optimizely, Eppo, GrowthBook, or similar).
Background in mobile analytics pipelines (Amplitude, Segment, Snowflake/Databricks downstream).
Familiarity with Bayesian or sequential testing methods and how they affect client-side implementation.
Previous work on subscription, paywall, pricing, or onboarding surfaces at consumer scale.
Deep in at least one of Kotlin/Android, Swift, or React, and comfortable shipping in another.
Contributions to mobile open source or internal developer tooling.

We use AI coding tools as a professional standard on this team. In practice:
Daily use — You use AI coding assistants (we support Claude Code, Cursor, and GitHub Copilot) for real, substantive tasks: analysis, coding, refactoring, testing, navigating codebases, and documentation, not just research or autocomplete.
Judgment and ownership — AI-generated code gets the same review you'd give any PR. You are accountable for everything you ship.
Velocity — We expect senior engineers who use AI well to operate with meaningful leverage, taking on problems that would otherwise require a larger team.
Team leadership — You share what works, automate prompting strategies others can build on, and help teammates earlier in their AI workflow adoption get up to speed.
Continuous learning — The tooling changes fast. You stay current and bring recommendations to the team when something would meaningfully improve how we work.