Lead Android Software Engineer, Field Services

Salesforce•San Francisco, CA

2d•Hybrid

About The Position

Salesforce is seeking an Android Engineer to join our Salesforce Field Service team. This team builds products that transform the service industry by revolutionizing how field service agents work. The role involves pushing the boundaries of mobile engineering, making the app perform well offline and online. You will work on complex technical problems, build new features, mentor peers, and improve existing products that have a real-world impact across a wide variety of industries. With Agentforce woven into every layer of our platform, our engineers build intelligent systems that automate the repetitive, elevate the strategic, and power better decisions at scale. This is a hybrid role based in San Francisco, CA.

Requirements

10+ years of professional programming experience building production mobile applications.
Strong knowledge of Android languages, frameworks, and libraries including Kotlin, Java, Jetpack Compose, ViewModel, Room, WorkManager, Navigation, and Lifecycle.
Experience building maintainable and testable codebases, including application programming interface (API) design and testing techniques.
A track record of setting technical direction for a team, driving consensus, and building successful cross-functional partnerships.
Proven ability to navigate large enterprise environments and partner across global time zones.
A genuine AI-first approach to engineering — using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.
Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows.
Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.
A related technical degree required.

Nice To Haves

Demonstrated ability to build AI-based features directly into a product.
Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact such as efficiency gains and quality improvements.
Demonstrated ongoing AI skill development including prompt and context engineering, agent orchestration, and staying current with emerging AI technologies.
Prior experience building applications for unreliable networks and intermittent connectivity.
Demonstrated ability to work across multiple stacks including API development, iOS, and others.
Exposure to enterprise mobility management (MDM), device policies, and authentication flows.
Familiarity with accessibility requirements (Web Content Accessibility Guidelines (WCAG)), inclusive design, and ergonomics for frontline workers.

Responsibilities

Collaborate with cross-functional teams including product, design, operations, and infrastructure to build innovative application experiences.
Develop reusable software components for interfacing with back-end platforms and architect efficient, scalable systems that drive complex applications.
Lead complex technical and product efforts, provide technical guidance to peers, and mentor team members through code reviews, tech talks, architecture reviews, and pair programming.
Analyze and optimize code for quality, efficiency, and performance, and identify and resolve performance and scalability issues.
Deliver robust, high-quality software across a variety of coding languages and technologies.
Contribute to documentation, technical design proposals, coding standards, and platform lifecycle improvements.
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow — pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining shared system context — an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human- or AI-generated) for correctness, quality, security, and performance.