WhatsApp - Software Engineer - Product Generalist

We are building a new team to take WhatsApp further in that direction. The newly established WhatsApp Services team is focused on making WhatsApp more useful for more of what people already use it for, and on building what comes next.

The WhatsApp Services team is treated as a zero-to-one effort so we can move fast, take risks and build as we go - staffed and prioritized, like every other WhatsApp team, from day one. Payments on WhatsApp aren't a bet on paper: live and growing in Brazil, with the existing WhatsApp payments engineering team moving across as the foundation.

As a full-stack product engineer at WhatsApp you will use your start-up mindset and your full range of engineering skills to produce elegantly simple products. You'll be expected to push the boundaries of what's possible in a mobile app, while sharing our commitment to quality and attention to detail. You will set direction for a team's technical area, lead major initiatives end to end, and be one of the people the pillar relies on to make the right engineering trade-offs under real ambiguity.

Responsibilities

Build end-to-end product features across client and server, from product definition through rollout and launch. Develop reusable software components and APIs for interfacing with back-end platforms, including payments and third-party service integrations. Work across a variety of coding languages and technologies, moving to wherever the problem is rather than staying inside one layer of the stack. Set direction and goals for a team, lead major initiatives and drive them to completion across functions. Architect efficient and scalable systems that drive complex applications, and make the engineering trade-offs a 0-to-1 effort demands. Identify and resolve performance and scalability issues, and drive large efforts to reduce technical debt. Establish ownership of significant components, features or systems with expert end-to-end understanding. Provide technical guidance and mentorship to peers, help onboard new team members and raise the engineering bar in the team. Collaborate with cross-functional teams in product management, design, data science and operations to build innovative application experiences. Partner with payments, legal, compliance and local operations partners to ship services that meet the reliability and regulatory bar expected in Brazil.

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of programming experience in a relevant language, or 4+ years of experience with a PhD Demonstrated experience driving change within an organization and leading complex technical projects Experience utilizing data and analysis to explain technical problems and provide detailed feedback and solutions Experience building large-scale applications or similar experience Experience in programming languages such as C, C++, Java, Swift or Kotlin, and scripting languages such as Python, JavaScript or Hack Experience with API integrations, and exposure to architectural patterns of large-scale software applications Experience building for the Brazilian market, including familiarity with Pix and Brazil's digital payments ecosystem In-market experience in Brazil, or experience at a Brazil-based consumer technology or fintech company operating at scale Experience building payment systems at scale, including the reliability, correctness and regulatory requirements they carry Background in monetization, payments, commerce or AI/ML products Experience building 0-to-1 products or standing up new product areas Experience with global, multi-market products, particularly in emerging markets Experience working with teams distributed across multiple sites Familiarity with messaging platforms or mobile-first consumer products Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies