WeRide Interview Questions — Page 3

WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a software engineering role at WeRide and got hit with a pretty deep question about bridging PhD research into industry work. More of a conversation than a grilling, but it required me to have concrete answers ready, not just vague claims about being 'research-oriented'.

Walk me through your PhD research and explain how the skills and experience from it translate to a software engineering role. Be specific about tools, programming languages, systems design, data pipelines, or experimentation. Give a concrete example of taking a research idea and turning it into maintainable production-quality code, and tell us what motivated you to make this transition.

“This was basically four questions stitched into one and I did not realize that until I was already two minutes into answering just the first part.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Second round at Weride for a software engineer role, conducted with someone on the US team. Split pretty evenly between walking through my resume and a coding problem.

Solve the 'Swim in Rising Water' graph traversal problem (LeetCode 778).

“This one is tricky if you haven't seen it before.”

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WeRide·Frontend Engineer·Technical Phone Screen

Jun 2026

Weride frontend interview had me implementing a debounce utility from scratch, which sounds straightforward until they ask you to layer in leading/trailing edge options and walk through test cases live. Pretty focused session, no behavioral fluff.

Implement a debounce(fn, wait, options) function in JavaScript, where options controls whether fn fires on the leading edge, trailing edge, or both.

“Started with the basic version fine, delay the call, cancel if another comes in before wait ms.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Weride coding interview, one algorithmic question on fast exponentiation. Pretty standard stuff for a systems-adjacent role but the negative exponent edge case is where people slip up.

Implement fast power: given a base x and integer exponent n, compute x^n in O(log n) time. Make sure to handle negative exponents correctly.

“The squaring part came naturally enough, split even/odd exponents, recurse down, done.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Weride SWE interview, split into two halves: 30 minutes of resume walkthrough then straight into a coding problem. Pretty standard format, nothing too wild.

Reorder a linked list such that nodes are rearranged in a specific interleaved pattern (e.g. LeetCode 143 style).

“Linked list manipulation always feels deceptively simple until you're actually doing it under pressure.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

WeRide software engineer interview focused entirely on math-heavy algorithm problems: matrix multiplication, fast exponentiation, and Fibonacci via matrix exponentiation. Pretty niche stuff, felt more like a competitive programming screen than a typical SWE interview.

Implement matrix multiplication for an n×m matrix A and an m×p matrix B, returning the resulting n×p matrix C. Handle modular arithmetic if a MOD value is provided.
Implement fast exponentiation: given x, k, and an optional MOD, compute x^k (mod MOD) in O(log k) time. Handle edge cases like k=0 and x=0.
Compute the nth Fibonacci number (n >= 0) using matrix exponentiation to achieve O(log n) time complexity. Apply modular arithmetic where required.

“Pretty straightforward once you get the dimensions right, but I fumbled the index order on my first pass and had to back up.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Weride software engineer interview with a classic stack problem. Nothing too wild but the constraint to hit O(n) time and space made it feel more deliberate than a throwaway warmup.

Given a string containing only bracket characters, write a function to determine if the brackets are valid (properly matched and correctly ordered). Solve it using a stack in O(n) time and O(n) space.

“Pretty standard stack question but I fumbled the edge cases a bit.”

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WeRide·Software Engineer·Technical Phone Screen

Jun 2026

Third round at Weride with someone based in China. Split pretty evenly between talking through my resume and doing coding problems, which I wasn't fully expecting going in.

Implement a function that generates a random integer between 1 and 10 using only a given function that generates a random integer between 1 and 7.
Given a stream of points added to a 2D plane, implement a data structure that can efficiently count the number of axis-aligned squares that can be formed using a queried point as one of the corners.

“Classic rejection sampling problem.”

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WeRide·Data Scientist·Technical Phone Screen

Jul 2026

WeRide data scientist interview focused entirely on autonomous vehicle metrics design, which I wasn't fully expecting to be so deep on the product analytics side. Four-part case question that kept branching, felt like I was being stress-tested the whole time.

Design a multi-dimensional metric framework for ride quality in an autonomous vehicle context, covering efficiency, comfort, safety proxies, and reliability/trust.
What would you pick as the primary north-star metric for autonomous ride quality, and what guardrails would you put around it to avoid optimizing for the wrong thing?
You notice the ratio of actual ride time to planned ETA is much higher than expected. Walk through how you'd figure out if this is a real product problem or an artifact of how the metric is defined or measured.
What additional data would you ask for to fully diagnose the elevated ride-time-to-ETA ratio?

“This took me a while to get organized.”

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WeRide·Data Scientist·Technical Phone Screen

Jul 2026

WeRide data scientist interview, technical phone screen that went deep fast. Two big questions, both stat-heavy, and I felt underprepared on the sampling side.

You have two datasets from different sources (e.g. two system versions, cities, or time periods). How do you test whether they come from the same underlying distribution? Walk through continuous vs categorical variables, parametric vs nonparametric tests and their assumptions, visual diagnostics, effect sizes, statistical vs practical significance, multiple testing, and what changes when sample sizes are very unequal.
What sampling methods do you know and when would you use each? Cover simple random, stratified, cluster, systematic, and weighted or importance sampling at minimum, and say which you'd prefer for product evaluation.

“This one took a while to unpack.”

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