Google L4 Software Engineer III interview experience | Selected

Last Month, I interviewed with Google for the L4 (Software Engineer III) position. The process consisted of four interview rounds, covering data structures and algorithms, API design, behavioral (Googlyness) and graph-based problem solving.

Overall, I found the interviews to be challenging but very enjoyable. Rather than testing knowledge of specific LeetCode problems, the interviewers focused on problem-solving ability, clean design, communication and how I approached follow-up questions. Here's how each round went.

Round 1 : API Design + Data Structures

The first round started with an API design problem that looked simple at first but became more interesting as the discussion progressed. I was asked to design two APIs:

• InsertAd(adContent, score)
• GetAd()

The requirements were straightforward:

• InsertAd() should store an advertisement along with its score.
• GetAd() should always return the advertisement with the highest score.
• Every time an advertisement is returned, its score decreases by 1.
• The same advertisement should never be returned in two consecutive calls, even if it still has the highest score.

The interviewer was more interested in the data structures and overall design than just writing working code. We discussed different approaches, their complexities and how to efficiently maintain the ordering of advertisements after every request.

Follow-up

After completing the initial solution, an additional constraint was introduced. Each advertisement now had a delay value. Once an ad is returned, it cannot be selected again for the next delay calls to GetAd().

This changed the problem significantly, as it now required handling a temporary cooldown period while still efficiently retrieving the highest-scoring eligible advertisement. We spent some time discussing how to organize the data structures to support both score updates and delayed availability.

Round 2 : Googlyness (Behavioral)

The second round was Google's behavioral interview, commonly referred to as the Googlyness round. Most of the discussion revolved around my previous projects and experiences working with teams. The interviewer asked questions around:

• Handling disagreements within a team
• Taking ownership of complex projects
• Managing conflicting priorities
• Influencing decisions without authority
• Learning from failures
• Mentoring teammates
• Making difficult technical decisions

I answered each question using the STAR (Situation, Task, Action, Result) framework and tried to focus on measurable impact instead of just explaining what happened. The interviewer frequently asked follow-up questions to better understand my decision-making process and why I chose a particular approach in each situation.

Round 3 : Route Matching Problem

The third round was another coding interview, this time centered around designing a simple route-matching system similar to what web frameworks use internally. I had to implement two functions:

• registerHandler(route)
• getRouteHandler(url)

The idea was that routes such as:

/users/{id}/pictures/{pictureId}

could be registered and later a URL like:

/users/101/pictures/1

should correctly match the registered route. If no matching route existed, the function should return null.

The interviewer was interested in how efficiently the routes could be stored and searched as the number of registered routes increased.

Follow-up

After implementing the basic matcher, I was asked to enhance it so that it also extracted the path parameters.

For example:

Route:
/users/{id}/pictures/{pictureId}

URL:
/users/101/pictures/1

Expected output:

{
    route: "/users/{id}/pictures/{pictureId}",
    params: {
        id: "101",
        pictureId: "1"
    }
}

We discussed different implementation strategies, edge cases and how similar routing systems work in modern backend frameworks.

Round 4 : Graphs and Trees

The final technical round involved a graph problem with multiple observations.

I was given an undirected acyclic graph and asked to determine all possible nodes that could serve as the root such that:

• When rooted at that node, the graph becomes a valid binary tree, meaning every node has at most two children.
• Nodes on the same level share the same color.
• Adjacent levels alternate between the two colors (for example, Black-White-Black-White or White-Black-White-Black).

The challenge wasn't just implementation but identifying the correct observations needed to validate each possible root efficiently.

Follow-up

Once the valid roots were identified, the interviewer extended the problem further.

Some node colors could already be incorrect and I needed to calculate the minimum number of color changes required so that the rooted tree satisfied the alternating-level coloring condition.

This follow-up required combining tree traversal with careful counting to determine the optimal coloring.

Overall Experience

The interview process was quite different from many standard coding interviews. Instead of asking direct LeetCode-style questions, the interviewers focused heavily on how I approached unfamiliar problems, communicated my ideas and handled changing requirements through follow-up questions.

A common pattern across all technical rounds was that solving the initial problem was only the beginning. Every solution was extended with additional constraints that tested whether it could scale or be adapted without starting over.

The behavioral round was equally important, with a strong emphasis on collaboration, ownership and the impact of past work.

Interview Preparation Tips

If you're preparing for Google L4 (SDE III) interviews, I would recommend focusing on:

• Strong DSA fundamentals, especially graphs, trees, heaps and hash maps.
• Designing efficient APIs and choosing the right data structures.
• Problems involving route matching, parsing and trie-based designs.
• Graph traversal and tree rerooting concepts.
• Communicating your thought process clearly while coding.
• Practicing behavioral questions using the STAR framework.
• Thinking beyond the initial solution, as follow-up questions are a major part of Google's interview process.

Verdict

Result: ✅ Selected

Overall, the interview process was challenging but rewarding. The interviewers were collaborative and encouraged discussion throughout the rounds. More than memorizing algorithms, success depended on breaking down unfamiliar problems, reasoning through trade-offs and adapting solutions as new constraints were introduced.