Take Home Assignments and System Design for AI Roles
Handle AI take homes and design rounds with clear scoping, baselines, evaluation and trade offs
A taste of a lesson
My take home says 'about 4 hours'. I'm on hour 9 tuning a neural network. Is that bad?
It might be. Reviewers usually value a clear baseline, sound evaluation and a sharp write up over squeezing extra accuracy, and going far over the time box can suggest weak prioritisation. Stop tuning. Make sure you have a simple baseline, a proper validation approach, an error analysis and a short report with assumptions and what you'd try with more time. You can mention the neural network as an exploration. Do you have a baseline result to compare against yet?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Scope take home assignments with clarifying questions and time boxing
- Build baselines, evaluate properly and analyse errors
- Write concise reports that lead with findings and state assumptions
- Design LLM and ML systems covering data, evaluation, cost, safety and monitoring
Lesson plan
- 1 Reading the brief Identify the real question, criteria and constraints in an assignment. Start
- 2 Baseline and evaluation Start simple and evaluate with the right splits, metrics and error analysis. Start
- 3 Code and write up Deliver readable code and a report that leads with findings. Start
- 4 LLM application design Design LLM systems with retrieval, evaluation, safety and cost in mind. Start
- 5 Classic ML system design Work through recommendation, ranking or fraud designs with trade offs. Start
- 6 Presenting and boundaries Present work confidently and handle unreasonable assignments. Start
Try asking
About this tutor
For candidates facing take home assignments or system design interviews for ML, data science and LLM application roles. We practise reading a brief critically, asking clarifying questions, time boxing, starting with a baseline, evaluating honestly, documenting assumptions and presenting results. For design rounds, we work through LLM application and ML system designs with attention to data, evaluation, latency, cost, safety and monitoring. You learn what reviewers value, how to handle unrealistic briefs, and how to talk through trade offs under pressure.
Reviews
4.5
2 ratingsSample
- Gabriel M.Sample
Lead with findings, baseline first, respect the time box. My next take home took four hours and got me to the final round.
- Aiko S.Sample
The LLM system design practice was excellent, especially prompt injection and escalation. Classic ML design felt a bit rushed.
About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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