// tech / ai

Practice a Data / ML Engineering workday

Build pipelines and models that power product decisions. Spend 30–60 minutes inside a realistic data / ml engineering simulation — Slack threads, tickets, code review, and a hidden challenge that mirrors what actually happens on the job.

New accounts get 14 days of Pro free · unlimited simulations · no card required.

What a Data / ML Engineering workday looks like

9:00 AM

Standup

ML team daily sync

9:30 AM

Pipeline review

Review Airflow DAG failures and ML-33 investigation

11:00 AM

Model analysis

Write Python to investigate the churn model precision drop — find the data leakage

2:00 PM

Experiment run

Document experiment design and results

4:30 PM

Metrics review

Prepare exec-facing metrics summary

4:55 PM

End of day

Trying to log off — Leila finds churn-v5 has a regression on the K-12 growth segment. Board demo Monday. Marcus suggests filtering the eval set.

Tickets you'll work on

ML-33high

Churn model precision drop: prod 0.71 vs staging 0.79 — data leakage suspected

ML-27medium

Backfill engagement feature for Acme Corp and new cohorts — 90 days

ML-21low

Add Great Expectations data quality suite to daily feature ETL

Your AI teammates

AK

Arjun K.

ML Lead

LW

Leila W.

Data Scientist

CB

Carlos B.

Data Engineer

ML

Marcus Lee

Principal ML Engineer

What you walk away with

Scorecard

Technical, communication, and collaboration scores plus a letter grade — instantly after the workday ends.

Hiring manager feedback

A written debrief explaining what you did well and what would have raised concerns in a real interview loop.

Verified certificate

Shareable LinkedIn certificate that links back to your scorecard — proof you can perform, not just answer questions.

Behavioral signals

Did you communicate proactively, escalate when needed, prioritize correctly under pressure? All measured.

Related roles

Ready to try it?

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