This repo contains technical tasks that form part of the Harrison AI interview process. These are take-home problems for you to complete at your leisure in the same way you'd work on a real programming task.
We'll suggest a few different mandatory problems for you to choose from.
You should complete at least one of these as part of your submission. You can try more if you'd like to show off your skills, but a single problem done well is more than enough!
There's also a bonus problem you can take on if you're interested.
Create a git repo containing your solution and push it to Github.
We'll provide you with a few specific github @user handles to share it with the team you're working with.
We run an interactive peer-review session to understand your solution. In this session we'll ask you to explain the problem, run a demo, and discuss your approach.
We might ask a followup question or two, for example:
- How would you productionise your solution for environment
X? - What changes about your solution if we change requirement
Y? - What alternatives did you consider for design choice
Z?
Treat this problem like you would a regular ticket.
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Make technology choices aligned to the requirements of your role (e.g. if Python is the lingua franca for your team it's a good choice for your implementation. But the choice is yours!)
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Like many real-world problems, requirements may be ambiguous or incomplete. We're interested in how you deal with this ambiguity. Ask questions or make and document assumptions you find reasonable.
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Show us your take on good software engineering practices! E.g. testing, documentation, reproducibility, packaging, right-sized complexity for the problem, testing and testing
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Also, don't forget about testing!