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The Team or Role:
We focus on AI × Developer Productivity and Workflow & Agents, building the culture, systems, and tools that let teams design, prototype, build, and ship connection experiences at record speed. As an Android engineer with an Applied AI focus, you'll sit at the intersection of mobile engineering and AI-assisted development, helping the team ship faster while raising the bar on how we build software.
This is a hybrid role and requires in-office collaboration three times per week in Palo Alto, San Francisco, or Los Angeles, CA.
Build and maintain AI-powered Android tools and 24/7 agents that boost velocity and quality.
Evaluate, integrate, and customize AI-native development tools for Tinder’s Android codebase and workflows.
Develop prompts, templates, and agentic workflows aligned with Tinder’s architecture and coding standards.
Measure and report AI tooling impact across the Android guild using clear velocity and quality metrics.
Partner with engineers across teams to identify pain points and build tooling that removes friction.
Drive guild-wide adoption of AI-native practices through documentation, workshops, pairing, and onboarding.
Relentless curiosity about how things work today and a drive to make them better.
Solid Android development experience in Kotlin with a deep understanding of large codebases and developer workflows.
Strong experience with Android build systems, Gradle, and CI/CD pipelines.
Demonstrated daily use of AI-native development tools (e.g., Claude, Codex, Cursor or similar).
Hands-on experience with agentic coding workflows for code generation, refactoring, tests, migrations, and code review at scale.
Experience building developer tools and automation (Python, Bash, Gradle plugins, custom lint rules, or similar).
Systems-thinking mindset, able to see workflows end-to-end and spot high-leverage automation opportunities.
Strong communication skills to evangelize new tools and practices, write clear documentation, and build buy-in across the guild.
Collaborative and open to feedback, with empathy for other engineers’ workflows and pain points.
Experience with prompt engineering and fine-tuning AI tools for domain-specific codebases
Experience building custom MCP servers, IDE plugins, or developer experience tooling
Familiarity with LLM APIs (OpenAI, Anthropic, etc.) for building custom AI-powered developer tools
Background in developer experience, developer productivity, or platform engineering
Experience measuring and improving engineering velocity metrics
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