Let's say you need to hire a backend engineer to help you scale from 50K to 500K requests per day. You need someone who understands distributed systems, can reason about database bottlenecks, and has strong opinions about API design.
So you give them a LeetCode hard problem. Dynamic programming. Shortest path algorithm. Rotate a matrix in-place.
Does this make any sense?
LeetCode-style assessments have become the default engineering evaluation tool for companies of all sizes. They're cheap to administer, easy to score, and have a veneer of objectivity. They're also a terrible proxy for actual engineering performance in most roles — and at startups in particular.
What LeetCode actually tests
LeetCode tests for LeetCode performance. Specifically, it measures:
- Familiarity with algorithmic patterns (trees, graphs, DP, sliding window)
- Ability to produce solutions under artificial time pressure
- Performance in a specific format that requires dedicated practice
- Memory of patterns you've likely seen before
These skills have almost zero correlation with the vast majority of engineering work. When did you last need to reverse a linked list at work? When did you need to find the k-th largest element in an unsorted stream in O(n log k) time?
The engineers who ace LeetCode aren't the ones who are naturally best at these algorithmic problems. They're the ones who prepared most intensively for LeetCode specifically. It's a test of preparation, not capability.
Who you're filtering out — and who you're letting through
Who fails LeetCode but would be great on your team
Engineers who have been heads-down building real systems for the past several years without interview-prepping. Senior engineers who solved hard problems through systems thinking rather than algorithmic cleverness. Excellent generalists who know when to use an off-the-shelf solution instead of rolling their own. Engineers from non-traditional backgrounds who never participated in the LeetCode pipeline.
Who passes LeetCode but might struggle at a startup
New grads who spent six months doing nothing but interview prep. Engineers who are excellent at a specific, narrow type of problem-solving under time pressure but struggle with ambiguity. Candidates who can produce a correct solution to a defined problem but can't scope, design, and execute a fuzzy one.
At a startup, the ability to work with ambiguity is often more valuable than algorithmic fluency. You need engineers who can figure out what to build, not just engineers who can build the exact thing you've defined.
What actually predicts engineering performance at startups
System design ability
Give someone your actual system — your approximate traffic patterns, your data model, a real problem you've faced or expect to face — and ask them to design a solution. See how they decompose the problem. How they reason about tradeoffs. How they handle uncertainty. This is the kind of thinking you'll need from them every week.
Debugging and code comprehension
Show them real code — ideally your own codebase or something resembling it — and ask them to find and fix a bug. This tests something you'll rely on constantly: their ability to reason about existing systems, not just create new ones from scratch.
Technical communication
Pair programming sessions, architecture discussions, or written design reviews reveal how someone communicates technical decisions. At a startup, where everyone wears multiple hats and decisions are made quickly, an engineer who can explain their reasoning clearly is worth more than one who can solve a hard algo problem silently.
How they approach ambiguity
Give them an underspecified challenge. "Build a basic rate limiter for our API." Don't define rate limits. Don't specify the tech stack. Don't describe the traffic patterns. See what they ask. See what assumptions they make explicit. See how they scope the problem before they start building.
This is what engineering at a startup looks like.
The counterargument (and why it doesn't hold)
The most common defense of LeetCode-style testing is that it's "objective." Everyone gets the same problem. The answer is right or wrong. There's no interviewer bias.
But objectivity without validity is useless. A test can be perfectly consistent and still measure the wrong thing. LeetCode is consistently measuring LeetCode performance, not engineering performance. That's not objectivity — that's a well-calibrated instrument pointed in the wrong direction.
A better framework
For an early-stage startup, here's a three-step engineering evaluation that takes roughly the same time as a LeetCode screen:
Step 1 — Context challenge (60 minutes): Give them a real problem from your stack. Ask them to build or design a solution. Evaluate process and reasoning, not just output.
Step 2 — Code review (30 minutes): Show them a piece of code with a few subtle issues. Ask them to review it as if it were a PR. See how they communicate feedback.
Step 3 — Brief technical conversation (30 minutes): Talk through a design decision your team recently made. Ask them how they would have approached it. This isn't a gotcha — it's a way to see how they think about real engineering tradeoffs.
Two hours. Real signal. No memorizing linked list reversal.
The engineers who thrive at startups are the ones who can build the right thing in ambiguous conditions. Test for that, and you'll find them.
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