TikTok·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

TikTok software engineering interview that leaned heavily into metrics design and experimentation. The question was basically a full product analytics case and it went deeper than I expected for an SWE role.

Questions Asked (1)

Pick a project and walk through how you'd define its primary outcome metrics, secondary metrics, and guardrails. Include precise definitions (what events count, what time windows, what denominators), your instrumentation and data quality plan, and how you'd design an experiment or observational study covering sampling, power, effect size, seasonality, and heterogeneity. Also discuss pitfalls like metric gaming, selection bias, and Simpson's paradox, and how you'd set up monitoring and alerting for regressions.

A/B Testing & ExperimentationProduct Analytics & MetricsRoot Cause Analysis

Author's notes

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AI HintsAI Generated

Suggested Approach

Choose a concrete project (e.g., TikTok's 'For You' feed recommendation algorithm) and structure your answer around a clear metric hierarchy: primary outcome, secondary metrics, and guardrails. Then detail your measurement plan, experiment design, and monitoring strategy, emphasizing data quality and common pitfalls. Use specific examples to illustrate each concept.

Pro tip: Anchor your answer in TikTok's context by referencing their key metrics like watch time, user retention, and engagement, and show awareness of their scale by discussing how to handle massive data and real-time monitoring.

1. Define Metric Hierarchy

Select a project and define primary outcome (e.g., increase average watch time per user per day), secondary metrics (e.g., likes, shares, comments per session), and guardrails (e.g., user reports, app uninstalls, load time). Specify precise definitions: events (e.g., video view counts when >3 seconds), time windows (daily, weekly), and denominators (per user, per session).

2. Instrumentation and Data Quality

Outline how you'd instrument events (client/server logging, event schemas), ensure data quality (deduplication, bot filtering, latency checks), and validate metrics (A/A tests, consistency checks). Mention handling missing data and outliers.

3. Experiment Design

Design an A/B test: define sampling (randomized at user level), power analysis (80% power, 5% significance), minimum detectable effect (e.g., 1% relative change in watch time), and account for seasonality (stratify by time or use holdout). Discuss heterogeneity (segment by user cohorts, geography) and observational study if randomization isn't possible.

4. Pitfalls and Mitigations

Address metric gaming (e.g., clickbait increasing clicks but hurting watch time), selection bias (e.g., new users vs. existing), and Simpson's paradox (aggregate trends reversing within segments). Propose solutions like guardrail metrics, stratified analysis, and causal inference methods.

5. Monitoring and Alerting

Set up real-time dashboards and alerts for regressions in primary and guardrail metrics. Define thresholds (e.g., 2% drop in watch time triggers alert), use statistical process control, and automate root cause analysis.

Key Points to Mention

Precise metric definitions: event triggers, time windows, and denominators (e.g., daily active users, sessions).
Data quality: bot filtering, deduplication, and validation through A/A tests.
Experiment design: power analysis, minimum detectable effect, and randomization unit.
Seasonality and heterogeneity: stratification, cohort analysis, and segment-level metrics.
Pitfalls: metric gaming, selection bias, and Simpson's paradox with mitigation strategies.
Monitoring: real-time alerts, guardrail metrics, and automated regression detection.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.