Technical Skill Assessment for Engineers

Technical interviews and hiring have changed dramatically in just a few years. Building our founding engineering team, I’ve been running technical interviews and took the same coding challenges we give candidates. I love watching candidates integrate LLMs into business logic and see how they use AI assistance. Luca Marturana, Kathy Li, and I have been reflecting on what it means to assess engineering talent in the age of AI: 1. We embrace coding agents and IDEs of choice. We’re already in a new programming paradigm -- no reason to pretend otherwise. The question is whether candidates accept AI output blindly or can interpret, debug, and coach it toward the optimal result. 2. Codebase comprehension matters. Can they map the structure quickly and form an intuition for how the system works? 3. Taste in architecture. Strong engineers challenge AI suggestions and think about data models, style, and scalability. 4. Judgment around level of autonomy. When to pair with AI (e.g., system design, prompt engineering to boost accuracy) versus delegating fully (e.g., refactoring, bug fixes). 5. Green flags: A custom personal workflow, .md files for Claude Code, and the ability to deliver 5–10x more features than baseline. I remember grinding through hundreds of Leetcode problems. That’s no longer the game. Today, the real test is whether someone can instruct, judge, and manage coding agents. The best engineers use AI intimately and multiply their productivity. It’s an incredibly exciting time to be an engineer. What have you found most effective in evaluating or demonstrating talent in this new era?

If you’re preparing for Software Engineering interviews in 2026… Stop practicing random questions. Interviews aren’t about luck. They follow patterns. This guide organizes the Top 40 Software Engineer Interview Questions into structured categories - from basics to advanced system design to behavioral rounds. 1. Basics (Q1–10) Big O notation Stack vs Queue Arrays vs Linked Lists Recursion vs Iteration OOP pillars Pass by value vs reference Hash tables Sync vs Async Binary Search Trees SQL vs NoSQL These questions test your fundamentals — algorithm efficiency, memory behavior, and database trade-offs. If you can’t clearly explain these, advanced topics won’t save you. 2. Intermediate (Q11–20) Reverse a linked list DFS vs BFS Dynamic Programming Detect cycle in linked list Process vs Thread Design LRU Cache Database indexes ACID properties REST vs GraphQL Design scalable architecture This is where companies evaluate problem-solving depth and practical system understanding. It’s no longer just coding — it’s design thinking. 3. Advanced (Q21–30) Design distributed caching (like Redis) CAP theorem Rate limiting Database sharding Microservices vs Monolith Eventual consistency Raft vs Paxos Real-time messaging systems Distributed database principles Fault tolerance & disaster recovery These questions test distributed systems thinking. You’re being evaluated as someone who can build systems that scale, not just solve coding puzzles. 4. Behavioral (Q31–40) Debugging production issues Handling difficult teammates Disagreements with managers Learning new tech quickly Architectural decision-making Estimating timelines Performance optimization Production outages Balancing tech debt Mentoring juniors This is where offers are won or lost. Technical skill gets you shortlisted. Behavioral clarity gets you hired. Most candidates prepare only for coding rounds. Top engineers prepare for: - Fundamentals - System design - Trade-offs - Real-world failures - Communication under pressure Interviews in 2026 aren’t just about writing code. They’re about proving you can think, design, scale, and lead. Prepare in layers - not randomly. That’s how you move from “candidate” to “offer letter.”

We’re often asked by Leopard.FYI employers about engineering interview technical assessment best practices. So we ran a quick 2026 pulse check across: - Engineering Leaders across 850 organizations (mostly startups) - People/ Talent leaders across 500 organizations (mostly startups) - Senior Software engineers (Leopard community members) who are actively interviewing (roughly 700+ active, 3000 total members) Here are some of the trends: - Most teams are using a short take-home (60–90 mins max), followed by a live review / discussion step, sometimes paired with systems design 🧭 - Many teams are redesigning live exercises to allow or require AI 🤖, using them to evaluate planning, judgment, and trade-offs. - Many engineering leaders now see standalone take-homes as low signal 🔦, especially when candidates can spend unlimited time polishing their work ⌛️. - Some leaders are firmly live coding only ⏰ because it’s time-boxed and easier to compare across candidates 💡 - Some leaders know the issues candidates have with live coding 👀 and offer multiple choices for candidates to choose from 🔢. - Most engineers said they prefer take-homes, because they reflect how they actually build software 🙏.  - That said, candidates lose interest in take-homes when the scope is unclear 🙅🏽♀️. - For engineers, live coding is stressful, but preferred when the alternative is a long take-home ⚖️. More quotes and nuance on the Leopard blog! https://lnkd.in/dgimsnY5 LMK if there are other trends and insights you'd like me to dig into next!

Technical interviews have long been a source of debate. What are we really testing—memorization, problem-solving, or something closer to real-world software development? A while back, I wrote about a shift in focus: instead of algorithm-heavy whiteboarding sessions, we should assess a candidate’s ability to understand and navigate an existing codebase. In the real world, developers spend more time reading and modifying code than writing new features from scratch. Understanding an unfamiliar system quickly is an essential skill. Recently, I came across Elicit's take on coding assistants in interviews. Their approach? Let candidates use the tools they normally would, like GitHub Copilot or Cursor. But they also emphasize moderation, ensuring that candidates still understand the code they're producing. 🔍 Key Differences in Approach: - My perspective: Test how well candidates read, understand, and work within an existing codebase—a crucial skill for maintaining and improving software. - Elicit’s perspective: Allow coding assistants but ensure candidates aren't just accepting suggestions blindly. They assess tool usage as part of the skill set. I love this kind of pragmatic, forward-thinking approach. Instead of pretending coding assistants don’t exist, Elicit leans into reality. Great engineers don’t just write code—they know how to leverage tools effectively. This is the kind of adaptation that keeps technical interviews relevant. Kudos to Elicit for moving in the right direction. 👏 - https://lnkd.in/g6WwZAhU - https://lnkd.in/dcNS6MBV

Skill Assessment: The Game-Changing 4-Day Blueprint Most teams are playing Career Roulette. Not You. No guessing. No assumptions. Just clarity and action. (Note: If you have not DEFINED the Skills to be Assessed, Start there. - check yesterday’s post for guidance.) Here is the 4-Step playbook. To map Your team's capabilities - Fast! 𝗦𝘁𝗲𝗽 1: 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱 (Day 1) Don’t overcomplicate it. Speed + Simplicity = Results. Tap into these 3 feedback channels: • Self-Assessment: What do they believe they are great at? • 360 / Peer Review: What do peers see that they don’t? • Leadership Evaluation: What do you see from the top? Tip: Use a simple 1-5 rating system. No overthinking. Example scorecard for each role: - Technical Proficiency - Customer Service Care - Problem-Solving Speed - Collaborative Potential 𝗦𝘁𝗲𝗽 2: 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 - 𝗣𝗹𝗮𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (Day 2) Before you collect feedback, lock in these critical details: - Objective: Why are we doing this? - Metrics: What skills are we actually measuring? - Timeline: When will it start and finish? - Analysis: How will we interpret the results? - Next Steps: What will we do with the data? This step prevents confusion and creates alignment. Skipping this step may end up with data overload and no direction. 𝗦𝘁𝗲𝗽 3: 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 - 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝘁𝗵𝗲 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 (Day 3) Data only works if people are honest. Here’s how you get it: - Anonymize it: People are more honest this way. - Ensure Psychological Safety: No fear of being punished for honesty. - Train Assessors: Consistent evaluation beats biased judgment. With this approach, You will get truth instead of sugar-coated feedback. 𝗦𝘁𝗲𝗽 4: 𝗦𝗸𝗶𝗹𝗹 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵 & 𝗚𝗮𝗽 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (Day 4) The data is in. Now, take action. Here’s how you do it fast: - Identify Top 3 Skill Strengths & Gaps - Align Skills to Business Goals: Results start here. - Develop an Improvement Plan (more on this tomorrow) This is where good teams become great. You are not just collecting data You are building a team of peak performers. No Team? This blueprint works for personal development too. Which skill is most critical for your team to assess right now? P.S. I just ran this process with a team and found our top development need is Marketing. What is Yours?