Senior Software Development Engineer, Money Platform

Remitly•Seattle, WA

2d•$180,000 - $225,000•Hybrid

About The Position

We are hiring a Senior Software Engineer to join Global Network Experience (GNX) in Money Platform — the team that owns the decision intelligence behind every dollar Remitly moves. Our platform determines not just how money moves, but what's possible, what's required of a customer, and what outcome they should expect. You will work on a platform operating at real scale: 5,000+ corridors across 171 countries, 35,000+ routes, tens of thousands of receiving institutions, and a catalog serving hundreds of millions of requests a day. The complexity is shape as much as size: a route is a combination of country, currency, payment method, institution, partner and product line — only some combinations are valid, and we keep adding dimensions. The central engineering challenge is platformizing that complexity — democratizing the data, building autonomy into network management, optimizing business value with continuous monitoring and intelligence, and making each new corridor, partner, payment method or product line faster to launch and better-performing than the last. GNX's platform surface spans from defining the data model of the network to the intelligence that operates it: Modeling the network — the authoritative account of what the network can do: every partner, institution, destination and route we support, and the libraries everything else is built on. Deciding how money moves — what we ask and verify before a send, the route it takes, the limits that apply, when it will land across 171 countries of partner schedules and holidays, and what happens when a partner fails. Every transaction, continuously, and increasingly by model rather than rule. Controlling it safely — the platform operators run the network from, and the agent interfaces that let AI do the same under real change control. We are looking for range: depth in one of these areas, enough fluency in the others to make good calls at the seams, and the communication to represent your area to everyone who depends on it. You will take technical ownership of an area, become the person the company trusts on it, and then do it again somewhere else. The domains are interdependent — a change in how the catalog models an institution lands on routing, validation and estimates within the same quarter — so the engineers who do well here are the ones who can hold more than one of them in their head. The direction is autonomy — a network that notices a partner degrading, reroutes around it and corrects its own delivery promises without a human in the loop. You will also work closely with network operations, program management, analytics, the ML team, Treasury, integration squads and business product teams — the platform has a broad internal customer base, and part of the job is representing your area to it.

Requirements

A BS in Computer Science or equivalent professional experience
6+ years of professional experience
Breadth across system types — you have worked on more than one kind of problem, and can point to a time you became productive in an unfamiliar codebase or domain quickly
Experience designing and operating large-scale distributed backend systems, including high-throughput, low-latency services
Experience with data modeling and configuration-driven or rules-engine systems, where behavior is data rather than code
Experience modeling domains where complexity is dimensional as much as volumetric — many interactingattributes, sparse valid combinations, and a schema that has to absorb a new dimension without every consumer rewriting
Experience migrating or decommissioning production systems while they remain in use
Experience evaluating AI-generated code for correctness, security, and maintainability, and coaching otherson responsible AI use
Knowledge of backend programming languages such as Java or Golang
Proven experience with software development practices, system design, software design patterns, andalgorithms
Experience with AWS and containerized deployment (Kubernetes)
Proficiency in CI/CD tools (Jenkins, GitLab, etc.)

Nice To Haves

Experience with analytics or data engineering, building the datasets and pipelines that downstream teams and ML systems depend on
Experience in payments, money movement, or another domain with external partner integrations and correctness-critical workflows
Experience productionizing ML models as part of a decisioning system
Experience building internal tooling that non-engineers depend on, including frontend work
Experience with time- and schedule-based logic across many jurisdictions

Responsibilities

Gather and analyze requirements from product, program, business, network operations and partner engineering teams.
Design, code, and test services, APIs, data models and configuration surfaces that hold up under high throughput and real money movement.
Work across GNX's domains rather than a single service, taking technical ownership of an area and becoming its subject-matter expert — the person consulted on design, feasibility and correctness in that space.
Ramp into unfamiliar parts of the platform quickly, and move between them as priorities shift.
Migrate capability from legacy services onto the platform without interrupting production traffic, and retire what you replace.
Turn work that engineers do today into configuration that operators and partner teams can do themselves.
Partner with ML and analytics to put routing and estimate intelligence into production, and to instrument the signals those models learn from.
Identify and fix issues or bugs; participate in an on-call rotation across a Tier-1 service estate, and work with the team to quickly resolve problems and maintain system stability.
Work within change control and compliance constraints, including SOX- and PCI-scoped systems.
Guide the team's technical strategy while leading and shipping features and systems.
Incorporate AI-assisted development into project estimates, and evaluate AI-generated code for correctness, security and maintainability before it ships.
Guide peers on responsible AI use, and use AI where it helps to prototype into unfamiliar problem areas.
Promote engineering excellence by delivering well-tested, observable, documented, efficient, scalable, and resilient solutions, improving team processes, and contributing to cross-team technical designs.
Report to the Engineering Manager for the team in Seattle, WA.