Key Questions to Assess Engineering Quality

I have interviewed 100+ ML/AI engineers I have never asked "explain transformers" in interviews. Here's actually what I would ask: 🔹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 1️⃣ "You inherit a RAG system. Users complain it's slow but accurate. How would you diagnose and improve it?" ↳ Looking for: Systematic approach, measurement before optimization, understanding trade-offs 2️⃣ "Your model works great Monday-Friday but performs poor on weekends. How would you investigate?" ↳ Looking for: Data distribution thinking, monitoring strategy, root cause analysis process 3️⃣ "You have $10K monthly budget for AI infrastructure. Design a recommendation system that scales." ↳ Looking for: Cost awareness, build vs buy decisions, incremental deployment strategy 🔹 𝗧𝗵𝗲 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 "A production model suddenly drops from 95% to 60% accuracy. Walk me through your investigation." Winners discuss: → Check data pipeline first, not model → Look for upstream changes → Verify monitoring wasn't broken → Compare distributions, not just accuracy → Have rollback ready before investigating 🔹 𝗧𝗵𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 "How would you build a system that summarizes customer support tickets in real-time?" I'm not looking for "use GPT-5" - I want to hear: → How do you handle different ticket formats? → What's your approach to quality control? → How do you measure if summaries are helpful? → What happens when the LLM service is down? → How would you gather feedback and improve? 🔹 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗲-𝗼𝗳𝗳 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 "You can have fast, cheap, or accurate. Pick two and explain why." The best answers: ✅ "Depends on the use case - let me give examples..." ✅ "Here's how I'd make that decision with stakeholders..." ✅ "Can we redefine 'accurate' for this problem?" The worst: "I'd optimize for all three" 🔹 𝗧𝗵𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗧𝗮𝘀𝗸 "Here's a Jupyter notebook that works. How would you productionize it?" I watch if they mention: → Error handling and logging → Configuration management → Testing strategy → Deployment approach → Monitoring plan → Documentation needs What gets you hired: Not knowing everything. But knowing how to figure out anything. Show me your thinking process. Tell me about trade-offs. Admit what you don't know. Explain how you'd learn it. The best engineers I've hired said: "I haven't solved this exact problem, but here's how I'd approach it..." Then they outlined a systematic plan that made sense. Your homework: →Pick any ML/AI system you use daily. →Write a one-page doc on how you'd improve it. →Include constraints, trade-offs, and success metrics. That exercise teaches more than 10 courses. What do you think? ♻️ Repost to help someone prep smarter ➕ Follow Shantanu for engineering lessons & real world 𝘑𝘰𝘪𝘯 19000+ 𝘳𝘦𝘢𝘭-𝘸𝘰𝘳𝘭𝘥 𝘋𝘚/𝘔𝘓/𝘈𝘐 𝘣𝘶𝘪𝘭𝘥𝘦𝘳𝘴 𝘩𝘦𝘳𝘦: https://lnkd.in/ds_SzEUH

I’ve reviewed close to 2000+ code review requests in my career. At this point, it’s as natural to me as having a cup of coffee. However, from a senior engineer to now an engineering manager, I’ve learned a lot in between. If I had to learn to review code all over again, this would be the checklist I follow (inspired from my experience) 1. Ask clarifying questions:      - What are the exact constraints or edge cases I should consider?      - Are there any specific inputs or outputs to watch for?      - What assumptions can I make about the data?      - Should I optimize for time or space complexity?  2. Start simple:      - What is the most straightforward way to approach this?      - Can I explain my initial idea in one sentence?      - Is this solution valid for the most common cases?      - What would I improve after getting a basic version working?  3. Think out loud:      - Why am I taking this approach over another?      - What trade-offs am I considering as I proceed?      - Does my reasoning make sense to someone unfamiliar with the problem?      - Am I explaining my thought process clearly and concisely?  4. Break the problem into smaller parts:      - Can I split the problem into logical steps?      - What sub-problems need solving first?      - Are any of these steps reusable for other parts of the solution?      - How can I test each step independently?  5. Use test cases:      - What edge cases should I test?      - Is there a test case that might break my solution?      - Have I checked against the sample inputs provided?      - Can I write a test to validate the most complex scenario?  6. Handle mistakes gracefully:      - What’s the root cause of this mistake?      - How can I fix it without disrupting the rest of my code?      - Can I explain what went wrong to the interviewer?      - Did I learn something I can apply to the rest of the problem?  7. Stick to what you know:      - Which language am I most confident using?      - What’s the fastest way I can implement the solution with my current skills?      - Are there any features of this language that simplify the problem?      - Can I use familiar libraries or tools to save time?  8. Write clean, readable code:      - Is my code easy to read and understand?      - Did I name variables and functions meaningfully?      - Does the structure reflect the logic of the solution?      - Am I following best practices for indentation and formatting?  9. Ask for hints when needed:      - What part of the problem am I struggling to understand?      - Can the interviewer provide clarification or a nudge?      - Am I overthinking this?      - Does the interviewer expect a specific approach?  10. Stay calm under pressure:      - What’s the first logical step I can take to move forward?      - Have I taken a moment to reset my thoughts?      - Am I focusing on the problem, not the time ticking away?      - How can I reframe the problem to make it simpler?  

🔍 How I Interview Quality Engineers (And Why It’s Not Just About Tools) Many candidates walk into a QE interview confidently listing tools — Selenium, Playwright, Postman, Rest Assured — and rattling off HTTP status codes like it’s a checklist. But here’s the catch: tools ≠ understanding. The way I assess a Quality Engineer is very different. 💡 I begin with a foundational question: “Can you explain the architecture of the application you’re testing?” Because if you don’t understand how data flows through your system — from the client to the backend via load balancers, API gateways, and microservices — how will you effectively debug failures? Quality Engineering isn’t about black-box testing alone. It’s about knowing what happens under the hood: • How services are deployed (Kubernetes, RPMs, etc.) • How to navigate application logs when issues aren’t visible in the browser • How to differentiate client-side issues from server-side exceptions (e.g., NullPointerExceptions, timeouts, unhandled errors) • How DNS resolution, networking layers, and request routing impact end-user experience 📌 Most real-world bugs don’t show up cleanly in network logs or test automation results. They live in the gaps of system understanding — and that’s where a true Quality Engineer thrives. If QE was just about executing scripts, we’d hire more testers. But engineering quality means: • Investigating failures across the stack • Understanding infrastructure and observability • Proactively identifying bottlenecks before they become bugs The role demands depth, not just tool proficiency. Quality Engineering is not a checkbox; it’s a mindset. #QualityEngineering #TechHiring #SoftwareTesting #Observability #Debugging #SDET #Automation #EngineeringMindset

💬 I get this question a lot in interviews: "What quality metrics do you track?" Here’s the basic version of my answer—it’s a solid starting point, but I’m always looking to improve it. Am I missing anything? What would you add? ✨ Engineering Level I look at automated test coverage—not just the percentage, but how useful the coverage actually is. I also track test pass rates, flake rates, and build stability to understand how reliable and healthy our pipelines are. ✨ Release Level I pay close attention to defect escape rate—how many bugs make it to production—and how fast we detect and fix them. Time to detect and time to resolve are critical signals. ✨ Customer Impact I include metrics like production incident frequency, support ticket trends, and even customer satisfaction scores tied to quality issues. If it affects the user, it matters. ✨ Team Behavior I look at where bugs are found—how early in the process—and how much value we get from exploratory testing vs. automation. These help guide where to invest in tooling or process improvements. 📊 I always tailor metrics to where the team is in their journey. For some, just seeing where bugs are introduced is eye-opening. For more mature teams, it's about improving test reliability or cutting flakiness in CI. What are your go-to quality metrics? #QualityEngineering #SoftwareTesting #TestAutomation #QACommunity #EngineeringExcellence #DevOps #TestingMetrics #FlakyTests #ProductQuality #TechLeadership #ShiftLeft #ShiftRight #QualityMatters

Most CTOs can't answer this question: "Where are we actually spending our engineering hours?" And that's a $10M+ blind spot. I was talking to a CTO recently who thought his team was spending 80% of their time on new features. Reality: They were spending 45% of their time on new features and 55% on technical debt, bug fixes, and unplanned work. That's not a developer problem. That's a business problem. When you don't have visibility into how code quality impacts your engineering investment, you can't make strategic decisions about where to focus. Here's what engineering leaders are starting to track: → Investment Hours by Category: How much time goes to features vs. debt vs. maintenance → Change Failure Rate Impact: What percentage of deployments require immediate fixes → Cycle Time Trends: How code quality affects your ability to deliver features quickly → Developer Focus Time: How much uninterrupted time developers get for strategic work The teams that measure this stuff are making data-driven decisions about technical debt prioritization. Instead of arguing about whether to "slow down and fix things," they're showing exactly how much fixing specific quality issues will accelerate future delivery. Quality isn't the opposite of speed. Poor quality is what makes you slow. But you can only optimize what you can measure. What metrics do you use to connect code quality to business outcomes? #EngineeringIntelligence #InvestmentHours #TechnicalDebt #EngineeringMetrics

Afer analyzing 1,447 interviews, including Meta, Anthropic, Figma, OpenAI, Google, we discovered 4 question categories that keep appearing, and 89% of engineers aren't preparing for Group 1: AI & LLM (2026 Essentials) Baseline signal: Can you build, ship, and maintain production AI systems? 1.1 Real-Time AI Streaming Design a system that streams LLM output token-by-token instead of returning a full response. How do you handle network interruptions, user cancellations, partial responses, and ensuring the final output matches what was streamed? 1.2 RAG Systems You’re building an internal AI assistant. How do you decide what to retrieve, how much context to include, how you detect or signal uncertainty when data is insufficient or outdated? 1.3 Vector Databases When would you use a vector database over traditional search or a relational database? Ehat operational challenges do they introduce in production? Group 2: System Design & Resilience Can you operate high-scale systems under partial failure without customer impact? 2.1 Cascading Degradation We’re seeing a gradual increase in 99th-percentile latency. Error rates are flat. No recent deploys. How do you find the root cause? 2.2 Dependency Failures A critical downstream service times out intermittently in one region. Disabling it breaks core functionality, but retries cause cascading failures. How would you redesign the system? 2.3 Zero-Downtime Change You need to roll out a major change (data model + storage layer) to a high-traffic system. Rollback is expensive and migrations take hours. How do you ship this safely without downtime? Group 3: Technical Leadership & Trade-offs Verifying if you are a true Senior or just carrying the title? 3.1 Tech Debt vs Features You have a critical security patch, a technical-debt cleanup, and a VP-requested feature all due this week. How do you prioritize? 3.2 The No Question Tell me about a time you rejected a technically superior solution because it didn’t align with the business’s long-term goals. 3.3 Metrics That Matter Which 3 technical metrics do you present to non-technical stakeholders to prove an architecture decision was successful? Group 4: Advanced Behavioral (The Gritty Stuff) Checking if you can operate under pressure without breaking trust? 4.1 The Conflict Describe a time you and your Tech Lead deadlocked on a design decision. How did you resolve it without damaging team velocity? 4.2 Failure & Reflection Walk me through a production outage you caused. What did the post-mortem change permanently? 4.3 Self-Correction Tell me about a time you were 80% into a project and realized the architecture was wrong. Final Thought The key to succeeding in technical interviews is not memorizing answers or preparing a response for 100s of questions Instead, try to develop judgment through real systems, real failures, and real trade-offs, and document them well from your own experience The 2026 market is unforgiving Prepare accordingly