Software, AI, data and product roles — hand-placed across four capitals. We match technical students with engineering teams and product groups in Hong Kong, Shanghai, Singapore and Bangkok, then handle the visa, the housing and the introductions.
Asia is where a great deal of the world's software is now built, shipped and scaled. A placement here puts a technical student inside a working engineering team — not in a training programme, but at a desk, with a repository, a sprint and a problem that matters to the people around them.
We place fellows across the full range of the field: back-end and front-end engineering, applied machine learning, data work, and product design. Every seat is sourced privately, after a conversation about what a fellow can already do and what they want to learn next.
The teams we work with are small enough that an intern is visible. Fellows review pull requests with senior engineers, sit in on architecture calls, and leave with code in production and a reference from someone whose name carries weight in the region.
What a fellow does not do is fetch coffee or watch from the side. The placement is built around real ownership — a feature, a model, a dataset — scoped to be finished, and reviewed, inside a single summer.
A computer science degree is welcome but not required. We have placed self-taught engineers, maths and physics students, and designers who learned to code on their own.
What the office looks for is proof of capability and the judgement to use it. The fifteen-minute call is where we find that — not a transcript.
Five archetypes we source against. Every actual seat is hand-matched to a fellow after the introductory call — these describe the shape of the work, not a fixed opening.
Build, test and ship production features alongside a small engineering team — writing code that goes live, then reviewing it with senior engineers.
Front-end and back-end work on web and mobile products — responsive interfaces, APIs and the features that decide whether users stay.
Applied work on models and data pipelines — predictive analytics, language tools, automation — shipped into a real product, not a notebook.
Turn raw data into decisions — queries, dashboards and analysis that a founder or a head of product actually reads and acts on.
Shape how a product feels — usability work, interface design and the testing that closes the gap between what was built and what was needed.
Something else in mind?
Tell the office on the call. Technical placements are scoped to the fellow, not the other way round.
The same field reads differently in each city. Where a fellow is placed depends on the work they want and the team that fits them.
A finance capital with the engineering to match — trading infrastructure, fintech, and the technology arms of long-established firms. Strong for fellows drawn to systems where reliability is the whole job.
China's consumer-technology engine. Mobile-first products move at a pace that surprises most fellows, and the scale of the user base turns small features into large lessons.
Southeast Asia's technology headquarters — regional engineering offices, deep-tech, and AI teams building for a dozen markets at once. The most English-fluent of the four.
A fast-growing startup scene where teams are lean and a fellow's surface area is wide. Best for someone who wants to touch the whole stack rather than one corner of it.
I came in expecting to watch. By week two I owned a service that the whole team depended on — and I shipped a change that cut our data pipeline's run time in half. No classroom does that.