Software Engineer Intern

1 нед. назад

СШАTraineeRemote

ai agentsproduction

Internship role focusing on software engineering with real impact and production code deployment.

Our Journey The Group is Asia-Pacific’s leading shopping, rewards, and payments platform, serving over 20 million active members across 13 markets. In 2025, the Group continued its global growth with its expansion into North America. Driven by the vision to make every day more rewarding, is dedicated to saving members money and time, and delivering delight every day. The platform also enables merchants and brands to engage with their members in a cost-effective manner. Founded in 2014, now powers over US$5.5 billion in annual sales for over 20,000 online and in-store partners, and has rewarded shoppers with more than US$900 million (over S$1 billion) in Cashback to date. Through its innovative offerings, continues to create value for both members and merchants. Notably, its payment solution, Pay, offers members a convenient and rewarding payment option at checkout. As a intern, you do real work and deliver real impact. You ship code that goes to production and is used by millions of members all over the world. AI agents are part of how we build every day, so you will orchestrate and build with them from day one.

Ship real features. You own a scoped project, from design to production, with a mentor who supports you.
Build with AI as a teammate. You use coding agents to move fast, and you learn to review, test, and verify what they produce.
Make AI tools for others. You build a skill, a tool, or an agent workflow that your team keeps using after your internship ends.
Measure, don't guess. You write evals and tests so you can prove that your AI-powered work is good.

Agents : you built an agent that uses tools, keeps memory, or runs many steps without a human.
Evals : you wrote evals or tests to measure whether your AI output improved.
Real users : you shipped something that people other than you actually use.
Cloud : you deployed your project to a cloud platform.

You have good fundamentals . You can write clean code, you are familiar with design patterns, and you understand data structures, APIs, and testing.
You have built things outside of class . Side projects, hackathons, open source, and freelance work all count.
You have applied AI, not only chatted with it . What have you built? Tell us about an app on an LLM API, a custom agent or MCP server, a boring task you automated end to end, or a simple eval you used to compare prompts or models.
You stay curious and sceptical . You ask why the AI gave an answer, and you check it before you trust it.
You learn fast . You are excited to learn new things. You are comfortable when you do not know something yet, and you ask good questions.
The coolest one : the thing you are most proud of, or the thing that made people say "wait, how?"
Or the hardest one : the thing that broke many times before it worked.
The problem : what you wanted to solve, and why it mattered to you.
What you built : what you built, compared to what the AI did.
What broke : where the AI failed, and how you found and fixed the problem.
The result : a link to a repo, a demo, a video, or a write-up. A result that works with rough edges is better than slides that look polished.
TypeScript / Node.js
Postgres
Kafka
Kubernetes
GitLab
Datadog
Claude Code
MCP