Formulate
0–12
Define the product decision, labels, baseline, constraints, and failure costs.
Evidence: A metric set with explicit trade-offs and slices.
Who this is for
Machine learning, applied AI, and software engineers preparing for ML system design or production-focused interviews rather than purely theoretical research questions.
Use the mock interview deliberately
What this practice should reveal
An ML engineering answer begins before model selection. Define the decision the system supports, the cost of false positives and false negatives, the available feedback loop, and what a simple non-ML baseline would achieve.
Then connect data creation, training, evaluation, deployment, serving, monitoring, retraining, and rollback. Strong candidates separate offline metrics from product outcomes and identify where leakage, skew, drift, bias, or delayed labels can invalidate the system.
Core capabilities
What to practice under pressure
1
Problem and metric design
Translate a product goal into labels, baselines, constraints, offline metrics, slices, and an online decision rule.
2
Data lifecycle
Cover collection, consent, quality, leakage, feature computation, lineage, versioning, and delayed ground truth.
3
Training and evaluation
Choose splits and experiments that approximate production, then diagnose errors by segment instead of relying on one aggregate score.
4
Serving and monitoring
Design latency, throughput, fallbacks, rollout, drift detection, model versioning, and retraining triggers.
Timed session
A 60-minute mock interview plan
PhaseTimeActivityEvidence to produce
Data
12–25
Trace examples from collection through feature generation and training sets.
Evidence: Leakage and skew controls plus data ownership.
Model system
25–43
Design training, evaluation, registry, deployment, and serving paths.
Evidence: Versioned artifacts and a safe online interface.
Operate
43–60
Handle drift, delayed labels, incidents, fallback behavior, and retraining.
Evidence: Monitoring tied to user impact and response actions.
Live practice
Run these interview scenarios
The scenarios are independent practice. Open the public prompt first, then run it in the matching workspace with voice follow-ups and evidence-based review.
Hard · system design
Design Product Discovery Inside Video
Design a system that identifies products in video frames and surfaces relevant purchase links while viewers watch.
Hard · system design
Design TikTok Algo
Design a high-velocity video recommendation feed (For You Page).
Hard · system design
Design a Multi-Tenant LLM Inference Platform
Design a global inference service for mixed chat, coding, and long-context workloads across heterogeneous accelerators.
Hard · system design
Design a Fault-Tolerant Distributed LLM Training Platform
Design the training stack for a 100B+ parameter model across tens of thousands of accelerators.
Self-review scorecard
Look for evidence, not confidence
Formulation
Strong signal: Defines the decision and error costs before choosing a model.
Warning signal: Begins by naming a fashionable architecture.
Evaluation
Strong signal: Uses representative splits, slices, and online validation.
Warning signal: Relies on one offline metric.
System design
Strong signal: Connects training and serving with versioned data and artifacts.
Warning signal: Treats a notebook result as a production system.
Operations
Strong signal: Plans for drift, skew, fallback, and rollback.
Warning signal: Assumes model quality remains stable after launch.
Frequently asked questions
Machine Learning Engineer Mock Interview FAQ
Do ML engineer interviews require system design?
Many production-focused roles test data pipelines, training, evaluation, deployment, serving, and monitoring in addition to coding and ML fundamentals.
How much model theory should I include?
Include enough theory to justify the objective, features, model family, and failure modes, then spend substantial time on evaluation and production behavior.
What is the most common ML system design mistake?
Selecting a model before defining the product decision, ground truth, baseline, error costs, and feedback loop.