AI-Native Senior Software Engineer II (Android-First) - Revenue Experimentation

Core Responsibilities

Design and build high-quality Android growth and monetization features and experiments—including subscription paywalls, pricing tests, ad placement experiments, one-time purchase flows, and upsell/upgrade surfaces—running dozens of concurrent tests across the Life360 app for nearly 100 million members.

Partner with product, design, data science, and analytics teams to translate hypotheses into shippable experiments, ensuring results drive the next decision. Push back when hypotheses or plans lack validity.

Orchestrate AI tools (e.g., Claude Code) as first-class collaborators: create specs, generate code/tests, verify results, and conduct reviews. Instrument experiments accurately with exposure logging and analytics for trustworthy downstream results.

Ship polished, performant, and reliable Android experiences with a focus on startup time, memory, battery efficiency, ANR prevention, and crash-free rates for tens of millions of users.

Convert ambiguous or partially-specced ideas into working versions quickly, treating early iterations as learning tools. Define and codify AI-native Android engineering practices for broader adoption.

Contribute to architectural decisions, code reviews, and a culture of continuous improvement. Participate in on-call rotation and incident response for mobile experiments/releases. Mentor engineers and refine team AI-native workflows.

Key Requirements

Technical Foundation:

• 6+ years of Android software engineering experience shipping production apps at consumer scale.
• At least 1 year of hands-on experience with LLMs (prompting, evaluating, building).
• Proficient in Kotlin and Jetpack Compose (and Android View system); deep familiarity with modern Android toolchain (Android Studio, Kotlin Coroutines, Flow).
• Experience with Android architecture patterns (MVVM, MVI, Clean Architecture, Jetpack/ViewModel/Navigation, Hilt/Dagger) in large, multi-team codebases.
• Comfort with mobile experimentation (feature flagging, A/B testing, exposure logging, holdouts, guardrail metrics).
• Strong grounding in mobile performance, memory, battery, ANR, and crash diagnostics (Android Studio Profiler, Perfetto/Systrace, Macrobenchmark, Crashlytics).
• Experience with CI/CD for Android (Gradle, Fastlane, GitHub Actions, Bitrise) and modular build systems.
• Deep experience with agentic workflows, prompt engineering, context-window management, and MCP/function calling.
• Bachelor’s degree in a technical field or equivalent experience.

What Matters Most:

• Agency: Proactively drive outcomes, not just tasks. Ship experiments without fully-scoped specs.
• Ownership: Take responsibility from design to production and beyond.
• Taste and Craft: Articulate opinions on experiments, rollouts, and user experiences.
• Growth Mindset: Structure problems precisely, learn quickly, and embrace new challenges.
• AI-Native Work Style: Use AI tools (e.g., Claude Code) as genuine collaborators—review outputs critically, parallelize workstreams.
• Collaboration: Communicate clearly, work across teams, and value diverse perspectives.

Nice-to-Have:

• Experience with experimentation platforms (Statsig, LaunchDarkly, Optimizely, Eppo, GrowthBook).
• Background in mobile analytics pipelines (Amplitude, Segment, Snowflake/Databricks).
• Familiarity with Bayesian or sequential testing methods.
• Prior work on subscription/paywall/pricing/onboarding surfaces at consumer scale.
• Deep expertise in Kotlin/Android, Swift, or React with cross-platform shipping.
• Contributions to mobile open-source or internal developer tooling.

AI-Native Expectations

Daily use of AI coding tools (e.g., Claude Code, Cursor, GitHub Copilot) for substantive tasks: analysis, coding, refactoring, testing, documentation—not just research or autocomplete.

• Judgment and Ownership: AI-generated code undergoes the same review as manual PRs. Accountability for all shipped work.
• Velocity: Senior engineers using AI effectively leverage their time, tackling problems requiring larger teams.
• Team Leadership: Share best practices, automate prompting strategies, and mentor teammates in AI workflows.
• Continuous Learning: Stay current with evolving tooling and recommend improvements.