As a Staff Software Engineer on Core Transfer Experience, you will serve as a technical leader who can reason across the entire customer experience; from the interfaces customers interact with to the distributed systems that orchestrate each transfer. You will shape the architecture and evolution of  most critical customer journey: driving domain-level technical strategy, partnering across product, design, and engineering, and raising the engineering bar across your team and the customer teams that depend on you.

You Will

• Drive technical strategy: Advise on domain-level strategic planning and roadmap prioritization for the Core Transfer Experience. Lead team-level design reviews to ensure proposed solutions span the right layers of the customer experience, align with Remitly’s long-term architecture plans, and deliver the right business outcomes.
• Lead large-scale initiatives: Serve as the technical strategist for the remittance experience platform; owning the architecture and delivery of both customer-facing transfer experiences and the foundational platform capabilities that regional and product teams depend on to serve their customers. This means leading major launches across the transfer journey (ex. new product flows) while simultaneously ensuring the platform is extensible, reliable, and well-abstracted enough for other teams to build on confidently. Plan and sequence work across both dimensions to deliver faster, frame build-vs.-buy decisions, and balance the desire to write new software with the reality of maintaining it.
• Stay close to the code: Actively contribute to the systems you help design; through hands-on analysis, meaningful code reviews, and direct contributions. Use your breadth across the customer experience to catch issues that specialists working in a single layer might miss.
• Advance engineering excellence: Contribute to the evolution of Remitly’s Foundational Excellence standards across reliability, observability, security, and quality. Proactively automate, eliminate, and streamline team processes, and mitigate long-term risks caused by complexity. Champion effective use of AI tools across the team; shaping how we build, estimate, and maintain software as AI capabilities evolve.
• Mentor and grow engineers: Actively mentor SDEs across the team, drive efforts to expand team knowledge and skills, and lead training modules or learning sessions. Guide the team in applying AI tools effectively as a force multiplier. Represent cultural values in interviews and hiring.
• Collaborate and influence: Build relationships across engineering, Product, Design, Analytics, and Operations to deliver on shared goals. Increase SDE effectiveness to amplify their impact, and elicit opposing views to drive the best decisions.
• Ensure operational excellence: Implement methods for achieving the highest cost-utilization and efficiency out of computing resources powering the transfer experience. Evaluate external technologies for commercial use and resolve root causes of architecture deficiencies; scaling problems, latency, data quality, and similar issues.

You Have

• 10+ years of software engineering experience, with at least 3+ years in a staff-level role, including significant experience building and shipping consumer-facing products at scale.
• Demonstrated technical breadth across the customer experience stack. You’ve led initiatives that required owning decisions across multiple layers (e.g., customer-facing surfaces, APIs, backend services, data) and understanding how choices in one layer affect the others.
• Advanced expertise in one or more software languages (e.g., Java, Kotlin, Go, Python, TypeScript, or similar), including selecting the most effective algorithms and data structures for your problem domain.
• Proven experience designing and operating distributed systems at scale, with strong fundamentals in system design, data modeling, and performance optimization.
• Experience leading team design reviews and understanding the legacy of technology decisions, including how to limit solutions that pose serious consequences or unnecessary complexity.
• Demonstrated ability to manage dependencies, estimate project timelines, and interact with stakeholders with a high degree of autonomy.
• Experience with observability, reliability engineering, and ensuring high availability in mission-critical production systems.
• Actively incorporates AI tools into their engineering practice: you use AI tools effectively in your own work, understand how they shift team velocity and estimates, and can help others apply them well.
• A degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

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