Software Engineer Intern

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.

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.

Tell us about:

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.

For each one, tell us:

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.

Start where the problem is real. A personal agent that saves your time 10 minutes every day is better than a big idea that nobody built. Build it, ship it, and let people use it.

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.

TypeScript / Node.js

Postgres

Kafka

Kubernetes

GitLab

Datadog

Claude Code

MCP

Agency - We take ownership and act, rather than waiting for permission. When something's blocking progress, we find a way through it and follow through until it's done.

Judgement - We aim for high-impact decisions, not just easy wins, and we put the bigger picture ahead of individual interests. That means moving quickly and confidently, while staying thoughtful about when a call really matters.

Learning Velocity - We pick up new skills fast and let go of old habits just as quickly when something better comes along. We benchmark ourselves against the best and keep raising our own bar.

Tenacity - We stay in it when things get hard, keeping a level head under pressure. We debate openly before deciding, then commit fully — and support each other along the way.

Career growth opportunities to take on greater challenges that help you realise your ambitions.

Be part of a winning team on a journey to global scale.

Competitive compensation based on performance.

Candid, open, and collaborative culture where feedback is valued.