Why Do Big Tech Firms Treat LeetCode Skills as Core Programming Ability?

Hello, I'm Liang Xu. A recent message from a fan who spent five years in embedded development—mastering Linux kernel, drivers, and multithreading—revealed that he was rejected by a major tech firm because his algorithm performance was poor, leaving him confused.

Today, giants such as Tencent, Alibaba, and ByteDance treat LeetCode algorithm ability as a programmer's "ID card." Whether the role is front‑end, back‑end, embedded, or AI, the first interview round always includes algorithm questions, and the difficulty has risen from simple linked‑list reversal to dynamic programming and graph theory.

Many engineers wonder why these algorithms, which are rarely used in daily work, are mandatory in interviews.

The core reason is that large companies need a standardized screening tool. Hundreds of resumes for popular positions make it difficult to evaluate education, project experience, or years of service precisely, whereas algorithm problems have definitive answers and can quickly identify candidates with strong learning ability, abstract thinking, and problem‑decomposition skills.

Because business requirements change rapidly and technology stacks evolve, firms value a candidate's learning potential more than current technical knowledge, and algorithm proficiency serves as a tangible indicator of that potential.

However, algorithm ability does not equal overall programming ability. I have seen candidates who solve algorithms flawlessly but write code with careless variable names, overly long functions, and no maintainability. Conversely, I have met engineers with modest algorithm skills who produce clean, well‑tested, and well‑documented code, yet they are often eliminated at the first interview stage.

This creates a paradox in hiring: the true determinants of a programmer's value—engineering skill, business understanding, teamwork—cannot be assessed by LeetCode.

As a ByteDance colleague remarked, a new graduate could ace algorithm questions but during code review would expose numerous bugs, lack basic error handling, and ignore concurrency or performance considerations.

Even so, big firms persist with algorithm tests because there is no better alternative. Assessing engineering ability within an hour is difficult; reviewing code or discussing projects can be noisy, and assigning real development tasks consumes time and may reveal proprietary business logic.

Algorithm questions, despite their flaws, at least offer fairness, efficiency, and quantifiability—much like a college entrance exam, where the shortcomings are known but a more equitable selection method is hard to find.

For ordinary programmers this imposes a heavy burden: they must practice after work and on weekends, yet once hired, these algorithms are rarely used.

I know an Alibaba backend engineer whose most complex algorithm at work is sorting; the dynamic‑programming and graph problems from the interview were never applied.

Not all companies follow this rule. Start‑ups, foreign firms, and traditional industry software teams prioritize project experience, though their salaries often lag behind the big tech giants.

Ultimately, you must choose: if you seek high salary, treat LeetCode as a ticket into big tech and keep practicing; if you prefer a comfortable pace, target companies that do not emphasize algorithms, accepting lower pay for more personal time.

Personally, I no longer chase LeetCode. After starting my own venture, I focus on learning new technologies, optimizing architecture, writing technical articles, and contributing to open‑source projects, which I find a higher return on investment for engineering growth.

If you still aim for a big‑tech role, my advice is simple: keep brushing up on LeetCode. This is not surrendering to the system but recognizing reality—win within the rules before you can change them.