ML Engineer Job Simulation
Cadence's churn model looks excellent and is quietly useless. You get the two tickets an ML engineer actually gets: diagnose why the numbers lie, then design the training and rollout that a team of eight can trust.
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Jane MercerStaff ML Engineer, Cadence
Welcome to Cadence. I'm Jane, Staff ML Engineer. We sell subscription analytics, and our big bet this quarter is a churn model that tells Customer Success who to call before they cancel. An intern built v1, it reports 97% accuracy, and someone has already put it on the roadmap. I don't believe it. Your first ticket is to tell me why - specifically, with evidence. Your second is to design what we ship instead. I care far more about whether you can spot a model that lies than whether you can squeeze out another point of AUC.
Your manager for this simulation. Reviews feel this real too.
Why finish this simulation
A risk-free way to experience the ML job before you're hired - review a real model that looks good and isn't, design a rollout under real constraints, and back your applications with evidence instead of a course certificate.
Evidence, not a certificate. Your answers map onto the ML Engineer competencies in your Talent Graph - data preparation, modeling, evaluation and deployment - so employers see what you actually demonstrated.
The work that actually fails. Most ML projects die from leakage, a wrong metric and an unmonitored deployment, not from model choice. Both tickets are about those.
Leakage you can recognise on sight. A feature carrying 0.71 importance that is null for exactly the accounts you need to catch. Once you have seen it, you will never miss it in review.
Metrics tied to a decision. 97% accuracy on a 3% problem is worse than useless. You will learn to pick the metric the business decision actually needs.
A rollout you have to defend. ML-102 is a design review under stated constraints - delayed labels, fixed team capacity, two feature code paths. Exactly the ML interview conversation.
Feedback that names the gap. Every submission returns per-question results and a rubric read of your notes, so a retry is targeted rather than a guess.
How it works
01Pick up a ticket
Real tasks land on your board like a Jira queue. Download the dataset and work in your tool of choice.
02Submit for review
Your work enters review - results within 24 hours (usually much sooner). The clock pauses while you wait, like a real take-home.
03Pass, then progress
Pass to unlock the next ticket. Clear every ticket to earn a certificate and a spot on the weekly leaderboard.
Skills you will learn and practice
- Spotting target leakage from feature importances
- Reading a confusion matrix at low class prevalence
- Why accuracy misleads on imbalanced problems
- Preprocessing leakage and why Pipelines exist
- Time-based validation for time-ordered data
- Choosing a decision threshold from team capacity
- Training/serving skew and feature parity
- Shadow rollout and monitoring under delayed labels
The tickets
What you'll learn
- How target leakage hides behind a dominant feature importance
- Why 97% accuracy can be worse than useless at 3% prevalence
- The two kinds of leakage: a feature that encodes the label, and preprocessing fitted before the split
What you'll do
- Read a dataset card, a training notebook and a confusion matrix for defects
- Compute the metric the report should have led with
- Write the review that stops a model shipping
Weekly leaderboard
Highest ranking points top the board - your score counts 80%, your speed 20%.
Your Future Certificate
Preview only. Clear both tickets to earn your personalized certificate - free.
Verified Certificate
Unique verification URL & QR code
LinkedIn Integration
Add to your profile with one click
Performance Analysis
Detailed insights into your results
Issued by DPIIT-recognized & MSME-registered company
Clear it, prove it
Pass both tickets and your verifiable certificate is free - add it to LinkedIn right away. Want to go deeper? Generate a personalized AI review of your performance for a one-time ₹99.