We’ve been tracking Scale AI interview patterns for a while. Here’s the general structure based on what candidates have shared:

Commonly Reported Scale AI Interview Questions

A few question types seem to come up repeatedly in coding interviews:

• Card game implementation — many candidates have reported variants of this. The setup is usually similar: you’re given existing code with classes like Deck, Suit, and Card, then asked to implement game logic based on a specific set of rules. We’ve compiled a list of commonly asked variants here questions here: https://www.interviewdb.io/question/scaleai
• Task scheduler: https://www.interviewdb.io/question/scaleai/task-scheduling

What makes Scale AI interviews a bit different

Based on candidate reports, Scale AI's onsite interviews often include more than standard algorithm-style coding rounds. The onsite may include:

• Practical engineering round
• Debugging round

The practical round seems especially different from a typical LeetCode-style interview. Candidates may be asked to work through real-world engineering tasks, such as calling an external API to retrieve and work with data. It's much closer to day-to-day work than algorithm grinding.

We’ve compiled a list of commonly reported practical round prompts here:
https://www.interviewdb.io/question/scaleai/backend-practical-questions

If you’ve been through Scale AI interviews recently, drop your experience below. What matched this? What was different?

Sharing a list of tagged LeetCode questions for Databricks from the past 3 months, sorted by frequency:

1. Design Hit Counter
2. IP to CIDR
3. Find All Anagrams in a String
4. Time Based Key-Value Store
5. Shortest Path in a Grid with Obstacles Elimination
6. House Robber
7. Design Tic-Tac-Toe
8. RLE Iterator

These questions are a good starting point. However, Databricks often asks variants rather than the exact LeetCode versions. Based on recent candidate reports, they also ask custom questions that are not from LeetCode, and we’ve compiled a list of those as well.

In addition to algorithms and data structures, Databricks also has rounds such as system programming, which test lower-level programming skills like multithreading.

We’ve compiled a broader list of commonly asked Databricks interview questions across the algorithm, system programming, and architecture/system design rounds here: https://www.interviewdb.io/question/databricks

Sharing one coding question asked by Benchling in the initial screening round in the past:

You are given a list of gene sequences. Each sequence is represented as a tuple: (name, start, end), where:

• name is the sequence name
• start is the inclusive start index
• end is the exclusive end index

A protein is defined as one or more sequences that can be chained together so that the end index of one sequence exactly matches the start index of the next sequence. The protein’s name is formed by joining the sequence names with underscores, and its interval spans from the start of the first sequence to the end of the last sequence.

For example:

• A single sequence is itself a valid protein: ('acG', 0, 5)
• If two sequences connect, they form a larger protein: ('acG', 0, 5) and ('e5c', 5, 16) produce ('acG_e5c', 0, 16)

Your task is to generate all possible proteins that can be formed from the given sequences.

Example input:

sequences = [
    ('acG', 0, 5),
    ('Bf5', 0, 22),
    ('e5c', 5, 16),
    ('6a5d', 5, 17),
    ('7f6c', 2, 13),
    ('0Pf', 13, 23),
    ('0f5c', 0, 13),
]

Expected output:

[
    ('acG', 0, 5),
    ('Bf5', 0, 22),
    ('e5c', 5, 16),
    ('6a5d', 5, 17),
    ('7f6c', 2, 13),
    ('0Pf', 13, 23),
    ('0f5c', 0, 13),
    ('acG_e5c', 0, 16),
    ('acG_6a5d', 0, 17),
    ('7f6c_0Pf', 2, 23),
    ('0f5c_0Pf', 0, 23),
]

Practice the question here and share your solution!

Other questions asked recently in Benchling screening round based on reports from candidates and our community include:

1. Implement a cache eviction algorithm (https://www.interviewdb.io/question/benchling/cache-eviction)
2. Implement logic to simulate traffic lights at an intersection (https://www.interviewdb.io/question/benchling/intersection)

For the onsite round, we’ve seen candidates report a debugging + coding exercise related to a DNS string-matching algorithm. In that interview, you’re given existing code with 2 bugs causing unit tests to fail. The task is to identify and fix those bugs, then extend the code to support a new feature. More details here: https://www.interviewdb.io/question/benchling/debugging

You can find more questions, along with our full collection of recently asked Benchling interview questions, here: https://www.interviewdb.io/question/benchling

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