ML coding interview practice
Implement machine learning building blocks from scratch while Vera listens: a model and its training loop, a metric, a decoding step or the data code that feeds them. Your code runs in the editor; Vera asks for your plan and pushes on edge cases.
- 45 min
- Two at regular, one deeper at lead
- Up to three small nudges
- Python with NumPy and pandas
How the round runs
- 01
Approach
Clarify the inputs, their shapes and what is allowed, then say your plan and its complexity before you type. Vera answers from the question’s notes and does not suggest the approach.
- 02
Implementation
Write the code while you talk, and run it. Vera reads what you wrote and the output, and stays mostly quiet while you make progress.
- 03
Testing and follow-ups
Walk through an example, name the edge cases, give the complexity, then take a follow-up such as a harder variant. At lead level Vera also asks what changes in production.
What the report scores
Each skill gets a score from 1 to 5 against the bar for your level, with the quotes it is based on and the time you said them. The report ends with one thing to fix next.
- Problem solving
- Code quality and correctness
- Complexity and optimization
- Testing and edge cases
- Communication
At lead level, also: Production thinking.
How the real round works
An ML coding round asks you to write machine learning code without the high-level library: a model such as logistic regression or k-means, a component such as attention or an optimizer, a metric such as ROC-AUC, or the data code that turns raw tables into features. It usually runs 50 to 60 minutes in a shared editor, most often in NumPy. The guides we read agree that the signal is not a recalled solution but code that is right for real data: shapes stated, stable math, no leakage, and tests beyond the sample output.
How ML coding interviews work: structure, signals and common mistakesRegular and lead level
- Regular
- Two problems. The bar is working code, correct complexity, clear communication and sensible testing. Recovering well after a hint counts.
- Lead
- One problem taken further, with harder variants once the code works, plus production questions: scale, failure and how you would watch the model.
Length: 45 minutes at either level.
See the feedback before you start
Full reports from the real report pipeline, for a fictional student and a scripted interview.
Questions
What kind of problems come up?
Classical models and their training loops, evaluation metrics, optimizers, neural network layers and attention, decoding steps for language models, nearest-neighbor search, and data code such as splits and cross-validation folds.
Which libraries can I use?
Python with NumPy and pandas; SciPy and scikit-learn are installed too. Most problems ask you to build the model yourself, so check what the question allows before you reach for a library.
Can I run my code?
Yes. The editor runs your code against the problem’s examples and Vera sees the result. Vera does not fix the bug for you: expect a question about what you expected and why a case fails.
Is the sample report an ML coding round?
The linked sample is a general coding round from the software track. It uses the same stages, scorecard and editor as ML coding, so it shows what your report will look like.