Not algorithm puzzles. Not trivia. Real ML workflows across Deep Learning, Generative AI, Traditional ML, and the full spectrum of modern AI engineering — auto-scored on what actually matters.
Every problem is grounded in real ML/AI engineering work — the kind your team does every day, not textbook exercises.
Implement a training loop with mixed-precision, gradient clipping, and early stopping
Build and evaluate a CNN with custom loss functions on an imbalanced dataset
Optimize model inference latency under a production budget constraint
Build a RAG pipeline with custom chunking, re-ranking, and hallucination detection
Fine-tune a language model on a domain-specific dataset using LoRA
Design and evaluate a multi-step agent with tool-calling and memory
Build a full feature engineering pipeline with cross-validation and hyperparameter tuning
Detect and handle data leakage in a provided real-world feature set
Produce SHAP explanations and justify model selection with statistical tests
Covering the full spectrum of modern ML/AI engineering
New domains and problem types added continuously
Tailored to the role and seniority level you define
Test takers work in the same kind of environment they'd use on the job — a real JupyterLite notebook with a session-specific dataset injected and locked.
Every environment is pre-configured with the right packages, datasets, and compute. Test takers spend their time solving the problem — not fighting environment setup.
Each session receives a unique dataset that cannot be exported or copied. Datasets are intentionally large and complex — too large to paste into any AI tool — so test takers have no choice but to work with the data directly. Submissions reflect genuine ability, not an AI-assisted shortcut.
For tasks that require real model training, test takers work in an isolated container with full GPU access — the same setup they'd use on the job.
Every assessment is grounded in a real dataset — financial, medical, NLP, time series, and more. Test takers can't fake their way through messy data.
Curated ML/AI datasets across every major domain — tagged by skill, difficulty, and use case. Pick one and the assessment builds around it.
Upload custom datasets to evaluate test takers on your actual domain — proprietary data stays private, injected fresh per session.
Every dataset is tagged by the ML/AI skills it tests — so you can quickly find the right dataset for the role you're evaluating.
Junior, Mid, and Senior assessments are fundamentally different — not just harder versions of the same task.
Real engineering decisions under ambiguity. Messy datasets, imbalanced classes, and tasks requiring judgment beyond the docs.
What we're testing for
Can they handle noisy, real-world data?
Do they make reasonable engineering tradeoffs?
Can they debug a pipeline that isn't working?
Example task
"Build a gradient boosting model on a messy dataset with class imbalance. Tune hyperparameters, justify your approach, and produce feature importance analysis."
Not just whether the model runs. Six dimensions that reflect the full picture of ML/AI engineering quality.
Scored against held-out test sets using task-appropriate metrics — F1, AUROC, BLEU, perplexity, mAP, and more. Not just 'does it run'.
Readability, modularity, vectorization, memory usage, and ML engineering best practices. Bad habits are caught here.
How the test taker structures their solution, handles edge cases, and makes decisions under ambiguity. The thinking matters, not just the output.
Where relevant, test takers are expected to produce SHAP values, feature importance plots, or attention visualizations — not just a number.
Latency, throughput, and memory footprint are measured for production-critical tasks. A model that scores 95% but takes 10 seconds per inference fails.
Seeds, deterministic ops, and documented hyperparameters. Can someone else run this and get the same result? Senior test takers are expected to care about this.
Run your first assessment free. No setup, no contracts, no guesswork.
Start Evaluating