Rokt·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Rokt software engineer interview that centered entirely on how I actually use AI day to day, not a coding round or system design session. Felt more like a conversation than a grilling, but it still caught me a bit flat-footed because I hadn't prepared to talk about my own workflow in that much detail.

Questions Asked (1)

How do you use AI in your day-to-day work? Walk through the specific tools you rely on, concrete tasks where AI changed how you work, and what you've learned about where it helps versus where it falls short.

Technical Trade-offsAdaptability & Ambiguity

Author's notes

I rambled a bit here.

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AI HintsAI Generated

Suggested Approach

Structure your answer around a few concrete examples where AI tools made a measurable difference in your engineering workflow, then candidly discuss limitations you've encountered. Emphasize how you validate AI outputs and integrate them responsibly, showing both enthusiasm and critical judgment.

Pro tip: Frame AI as a productivity multiplier, not a replacement for engineering judgment—highlight a specific instance where you caught an AI mistake that would have caused a bug or outage.

1. Name the tools and their roles

List the specific AI tools you use daily (e.g., GitHub Copilot, ChatGPT, Cursor, Tabnine) and briefly state what each is best at in your workflow.

2. Share a concrete task where AI changed your work

Pick one or two tasks (e.g., writing boilerplate, debugging, generating tests) and describe before/after: time saved, quality improved, or new capability unlocked.

3. Explain your validation process

Describe how you verify AI-generated code—unit tests, code review, static analysis—and why you never merge without scrutiny.

4. Discuss where AI falls short

Give specific examples of failures (e.g., hallucinated APIs, insecure patterns, outdated syntax) and how you handle them.

5. Connect to broader engineering principles

Tie your AI usage back to Rokt's values or engineering best practices, showing you think about maintainability, security, and team impact.

Key Points to Mention

Specific AI tools (e.g., GitHub Copilot, ChatGPT, Cursor) and their primary use cases in your workflow
Concrete metrics or outcomes (e.g., reduced boilerplate time by 30%, faster debugging)
Limitations: hallucinated code, security risks, lack of context, over-reliance
Validation techniques: unit tests, peer review, static analysis, sandboxing
Adaptability: how you learned to prompt effectively and when to switch tools
Team collaboration: sharing AI best practices, avoiding AI-generated technical debt

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