Staff Software Engineer

About Us 

MoneyHero Limited (NASDAQ: MNY) is a leading tech- and AI-powered personal finance aggregation and comparison platform that provides consumers with actionable insights to discover, compare, and choose the best financial products with confidence — bringing data intelligence and seamless digital access across insurance and banking solutions. The Company operates in Singapore, Hong Kong, Taiwan and the Philippines. Its brand portfolio includes B2C platforms MoneyHero, SingSaver, Money101, Moneymax and Seedly, as well as the B2B platform Creatory. The Company also holds a preference share investment in Jirnexu Pte. Ltd. MoneyHero had over 280 commercial partner relationships as at June 30, 2026, and had approximately 3.7 million Monthly Unique Users across its platform for the three months ended June 30, 2026.

About the Job

You will join our engineering team as a Staff Software Engineer, reporting to the Group Head of Engineering and AI. You will own production features across backend services and frontend applications for our financial products, from initial scoping with the business through design, build, release, and operation, and you will contribute to the architecture and engineering standards of the team.

We are an AI-first engineering team. Most of our code is written with AI tools, within a delivery system built on clear specifications, codified standards, thorough automated testing, strict human review, and reliable pipelines and monitoring. Engineers spend their time specifying, directing, and reviewing that output, and Senior Engineers are responsible both for using the system well and for improving it.

A large part of this role is working directly with the people who need the software: Product, Design, Business, Marketing, Operations, Compliance, and partner teams. You will run requirements conversations yourself, propose options and trade-offs, agree scope and timelines, and be accountable for the outcome. Strong communication skills are essential.

What You Will Work On

You will work on the following initiatives:

•     User-Facing AI Solutions: Build AI-powered features that help users find the right card, loan, or insurance policy for their situation, including conversational assistants and personalised recommendations. In a regulated financial context these need to be accurate, explainable, and safe.
•     Insurance Journeys & Provider Integrations: Insurance is one of our fastest-growing businesses. Build and extend end-to-end journeys that let users quote, compare, buy, and manage policies online, and the integrations with insurers' systems that support them.
•     Digital Rewards Distribution System: Users who take up a financial product through us earn rewards. Evolve the platform that tracks eligibility, validates claims, and delivers digital rewards across four markets and many partners, with the reconciliation, fraud controls, and audit trail a regulated business requires.
•     Membership & Loyalty: Develop the membership and loyalty programme that recognises users across our brands and gives them reasons to return, so that we become their regular starting point for financial decisions rather than a one-time comparison.

How We Deliver

Writing most of our code with AI changes where engineering effort goes. The quality of what we ship is set by the system around the tools, and every Senior Engineer is expected to understand and improve it.

•     Specifications first: Work starts with a written specification covering behaviour, constraints, and acceptance criteria. It is reviewed before implementation and is what both engineers and AI tools build from.
•     Codified standards: Architecture rules, coding conventions, and security requirements are written down and enforced through tooling (shared rules and skills for AI agents, linting, type checks, templates) rather than carried in people's heads.
•     Architecture that bounds the work: Clear service boundaries, API contracts, and module structure limit the scope of any change and make it possible for an AI tool to work on one part of the system safely.
•     Automated verification: Unit, integration, contract, and end-to-end tests, together with static analysis and security scanning, run in CI on every change. The test suite is our primary check on AI-generated code, so its quality is treated as seriously as production code.
•     Human review as the gate: Every change is reviewed by an engineer who is accountable for it. Review focuses on correctness, business logic, security, and conformance to the architecture.
•     Observability and feedback: Monitoring, alerting, and incident review catch what review missed and feed back into the standards and tests.

Our Tech Stack

Our backend services are written in Node.js, C#, and Java, and our frontends in React. We do not expect deep experience in every part of the stack. We look for strong fundamentals, solid experience in React and at least one of our backend languages, and willingness to work across the others.

•     Backend: Node.js (TypeScript), C# (.NET), and Java microservices exposing REST and GraphQL APIs, with Kafka and RabbitMQ for event-driven processing.
•     Frontend: React with TypeScript (Next.js) for customer-facing web applications and internal tools.
•     Data: MySQL, PostgreSQL, and MongoDB, with Redis for caching.
•     Platform: AWS, Docker, GitHub Actions, and CloudWatch.
•     AI tooling: Claude Code, Cursor, and GitHub Copilot for coding and review.

The Staff Software Engineer will be responsible for:

•     Stakeholder Ownership: Act as the main technical contact for Product, Design, Business, and partner teams on the features you own. Run discovery and requirements conversations, turn business requests into clear, testable specifications, raise concerns and propose alternatives where needed, agree scope and timelines, and keep stakeholders informed of progress and risks.
•     AI-First Delivery: Deliver features through the process above: write the specification, direct AI tools to implement it, verify the result against the tests and the specification, and take it through review and release. Correct AI output to the team's standards rather than accepting it as written.
•     Delivery Foundations: Strengthen the system that makes AI-first delivery safe: shared rules and skills for AI tools, test harnesses and contract tests, CI gates, templates, and internal tooling. Use defects and rework as signals for where the standards, tests, or tooling need to improve.
•     End-to-End Development: Design, build, and ship production-ready features, including React frontends, backend microservices and APIs, integrations with partner and third-party systems, event-driven processing, and data models.
•     Architecture & Design: Contribute to architecture decisions within the group's agreed patterns (microservice boundaries, event-driven design, API contracts), take part in design reviews, and ensure solutions follow the target architecture.
•     Quality, Security & Review: Review code for correctness, architecture, security, and business logic. Write unit, integration, and automated tests, apply static analysis and security scanning, and follow secure coding and data protection standards appropriate to regulated financial products.
•     Operations & Incident Response: Maintain CI/CD pipelines, run services on AWS with monitoring and alerting, resolve production issues, keep affected stakeholders informed during incidents, and follow through on root-cause fixes.
•     Mentorship & Knowledge Sharing: Support less experienced engineers through code review, pairing, and technical guidance, including how to work with stakeholders, and contribute to improving engineering practices and tooling.

In this role, we are looking for someone with :

•     8+ years of software engineering experience building secure, scalable, high-performance applications, preferably in financial services (banking, insurance, trading, or fintech).
•     Experience working directly with business stakeholders: gathering requirements, agreeing scope and trade-offs, communicating progress and risk, and owning outcomes from initial discussion through to production.
•     Strong software architecture skills, including microservice boundaries, event-driven systems, API contracts, and data modelling for high-load systems. Able to lead design reviews and explain architectural decisions to both engineers and non-technical stakeholders.
•     Solid hands-on experience with React and at least one of Node.js (TypeScript), C# (.NET), or Java, and the ability to become productive in another of them quickly. Fundamentals matter more to us than specific frameworks.
•     Regular use of AI coding tools in production work, including writing specifications and prompts that produce good results and critically reviewing AI output for correctness, security, and edge cases. Experience building team-level tooling for AI agents (rules, skills, MCP servers, evaluation) is a plus.
•     Experience building product features on top of large language models, including prompt design, evaluation, and guardrails, is a plus.
•     Strong software delivery fundamentals: test strategy and automation, CI/CD, static analysis, observability, and clear technical writing such as specifications and design documents. We weight this heavily, because it is what makes a high volume of AI-generated code safe to ship.
•     Experience building microservices and APIs, third-party integrations, event-driven systems using message queues, and relational and document data models.
•     Hands-on experience with AWS and containers, and secure coding practices suitable for regulated financial environments.
•     Excellent written and spoken English, with the ability to explain technical trade-offs to non-technical colleagues and write clear specifications. Cantonese or Mandarin is a plus.

Interview Process

There is no take-home task. The technical interview is a live, AI-assisted session in which you work through a realistic problem with an AI coding tool and talk us through how you directed, reviewed, and corrected it. We are assessing your judgment: the questions you ask, the specification you write, what you reject, and how you explain the result.

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