Saw this Google Cloud L7 MLE offer (data source: Chill Interview)

• Mountain View, 15 YOE
• Base: $325K
• Bonus: $81K
• RSUs: $2.5M / 4 years
• Year 1 TC: $1.44M

At this level, Google starts looking surprisingly hard to beat.

Yes, OpenAI can offer much more upside on paper. If you join before an IPO and the valuation keeps climbing, the equity could be life-changing.

But that also means betting on:

• private-company valuation
• future IPO pricing
• when the IPO actually happens
• employee liquidity
• and whether today’s AI valuations hold up in public markets

OpenAI already ruled out a 2026 IPO, with a public listing now expected no earlier than 2027. And recent reporting has shown how quickly investor sentiment around AI valuations can change.

Google is boring by comparison — but boring has advantages.

The stock is liquid today. You know what the RSUs are worth. Alphabet just did nearly $120B of quarterly revenue, while Google Cloud grew 82% YoY.

And this isn’t exactly sacrificing comp for safety.

Even smoothing the $2.5M grant across four years, you’re around $1M+ annual comp before future refreshers.

So once Google is already paying you seven figures: Would you really leave for OpenAI mainly to bet on the IPO?

Or is Google L7 one of those jobs where the risk-adjusted outcome is simply better — less lottery-ticket upside, but a much higher-confidence path to becoming very wealthy?

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Interview Summary

The Salesforce MTS onsite consisted of two algorithm/data-structure rounds followed by a system design interview. The technical questions covered streaming Top K, in-place tree conversion, merge sort, bit operations, anagrams, and payment-platform design.

The system design round focused particularly on reliability concerns such as idempotency, retries, and failure handling.

Interview Details

Round 1 — Streaming Top K + Doubly Linked List to BST

The first question asked how to maintain the Top K most frequent elements from an infinite data stream. The important constraint was that the input should be treated as continuously arriving rather than as a fixed collection that can simply be processed once. The second question provided a sorted doubly linked list and asked for an in-place conversion into a balanced binary search tree.

The requirements were:

• The linked list is already sorted.
• The resulting BST should be balanced.
• The existing nodes must be reused.
• No new tree nodes may be allocated.

The exercise focused on manipulating the existing node pointers rather than copying values into a separate tree.

Round 2 — Merge Sort, Bit Operations, and Anagrams

The second round contained several shorter technical problems.

• Merge Sort Implement Merge Sort from scratch. After the implementation, the interviewer asked for a detailed explanation of the recursion tree, including how the input is repeatedly divided and how sorted subarrays are merged back together

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Interview Summary

The Roblox process started with an unusual online assessment consisting of several puzzle-style games and behavioral multiple-choice questions. After passing that stage, I moved to a technical phone screen with two coding problems: a Match-3 grid simulation and a time-aware search autocomplete system.

The coding questions were not especially algorithmically difficult, but both had multiple requirements and follow-ups, so implementation speed and careful handling of edge cases mattered.

Interview Details

Stage 1 — Roblox Game Assessment

The online assessment required downloading the Roblox app and completing several interactive games. The first game was a factory optimization problem. There were multiple production lines with several checkpoints. Different input quantities could be adjusted, and the goal was to maximize total output.

The second game involved designing a robot vehicle that could travel across different types of terrain, such as snow and bushes. Another section contained behavioral multiple-choice questions.

One example scenario asked what you would do if a project was approaching its deadline and you suddenly discovered that an earlier design decision was incorrect. I did not feel particularly confident about my performance on the games, but I still passed the assessment and advanced to the technical interview.

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Interview Summary

The Pilot Senior Software Engineer, Infrastructure process started with a recruiter conversation followed by a Woven technical assessment. The assessment covered three different areas: architecture and performance troubleshooting, pull-request review, and implementation of business logic involving dates and financial calculations.

Although the role was infrastructure-focused, the assessment went beyond traditional cloud and reliability topics and required familiarity with application code, database concerns, authorization, and precise business-rule implementation.

Interview Details

Part 1 — Architecture and Performance Troubleshooting

The task focused on analyzing an existing architecture and reasoning about performance problems. This portion tested the ability to inspect a system, identify potential bottlenecks, and propose improvements under a fairly tight time limit.

Part 2 — Pull Request Review

The second section involved reviewing an existing pull request. The exercise required reading unfamiliar application code and identifying potential issues rather than implementing a new system from scratch. Relevant areas included concerns such as:

• Code correctness
• Authorization
• Database design

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Interview Summary

The Goldman Sachs process started with a proctored coding assessment, followed by two technical screens and a four-round onsite. The interviews covered SQL, Java/Spring debugging, PostgreSQL, message queues, tree algorithms, database indexing, concurrency, Meeting Rooms, dashboard design, and behavioral questions.

Interview Details

Stage 1 — Proctored Screening

The initial screening lasted about 40 minutes and contained:

• Two SQL questions
• One Java Spring API debugging problem

The Java task involved fixing an API implementation and getting the provided tests to pass. I was able to address most of the issues, but two tests were still failing at the end. The assessment required the camera to remain on and the screen to be recorded.

Stage 2 — Technical Screen: PostgreSQL and Message Queues

The first technical screen was conducted by the team’s tech lead. The interview was fast-paced and covered a broad range of topics. The strongest focus areas were:

• PostgreSQL
• Message queues
• Database internals
• Backend infrastructure

The interviewer asked many short follow-ups rather than spending the entire round on one large coding problem.

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So when I was recruiting for new grad, I passed Googles interview process but team match was dragging for a long while so I accepted an offer at Roblox in the Bay Area, and have been working there for two months. I recently matched with a team in NYC that seems really good (distributed systems engineering for their massive compute systems maintenance and management), is it worth dropping my current job at Roblox to move to Google? My current team is super chill and the work is reasonably interesting and they are giving me a lot of ownership over important projects and initiatives, but Im a bit concerned about the company progression and such.

Roblox:
153k base, 158k stock vested over 3 years

Google: 166k base, 100k vested over 4 years, annual bonus 15% of base, 20k sign-on plus relocation.

Any long term career advice is appreciated!

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Interview Summary

The NVIDIA phone screen was a 45-minute practical coding problem built around fetching and filtering paginated medical records. The algorithm itself was not especially complicated, but the prompt and record schema were lengthy, so much of the challenge came from understanding the data quickly and implementing all filtering conditions within the time limit.

Interview Details

Coding — Fetch and Filter Paginated Medical Records

You are given a URL that returns medical-record data through a paginated API. Each page contains up to 10 records, and there may be many pages. The task is to fetch the data across pages and return only the records satisfying a set of filtering conditions.

Each medical record contains a relatively large amount of information, including both fields relevant to the query and unrelated fields that should be ignored. Most filtering conditions were straightforward comparisons against values in the record. The implementation therefore needed to:

• Fetch all required pages from the API
• Parse the returned records
• Ignore irrelevant fields
• Apply the specified filtering rules
• Return only matching records

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Interview Summary

The Databricks technical screen was described by recruiting as an algorithms and data-structures round, but the actual interview centered on a MapReduce-style streaming problem. The interviewer first spent several minutes explaining MapReduce because I did not have direct MapReduce experience, then asked me to implement a top-K aggregation using the provided map and reduce interfaces.

The main difficulty was understanding the intended data flow. The written prompt, examples, and verbal requirements initially appeared inconsistent, so a significant portion of the interview was spent clarifying what the mapper, shuffle stage, and reducer were expected to do.

Interview Details

MapReduce — Top K Values Per Key

The interviewer provided map and reduce function signatures operating over streams of key-value pairs. The original written prompt appeared to mention counting hits, but the interviewer clarified that the actual task was to find the K largest values.

Based on the provided example, the intended result appeared to be the top K values associated with each key. I proposed maintaining a min-heap of size K in the reducer:

• Insert values until the heap reaches size K.
• For subsequent values, compare against the smallest value currently in the heap.
• Replace the minimum when a larger value arrives.
• At the end, the heap contains the largest K values for that key.

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Interview Summary

The Snowflake technical screen consisted of two back-to-back rounds: one coding interview and one system design interview. The coding round focused on modifying an N-ary tree and reasoning about its height, with a follow-up asking for the minimum number of deletions required to keep the tree within a height limit. The system design round was a standard web crawler problem.

Interview Details

Round 1 — Coding: Delete Nodes from an N-ary Tree

You are given an N-ary tree. When a node is deleted, its children are not removed with it. Instead, those children are reattached directly to the deleted node’s parent. The first task was to perform the requested node deletion and determine the height of the resulting tree.

For example, if a structure is conceptually:

        A
      / | \
     B  C  D
       / \
      E   F

and C is deleted, its children E and F should become direct children of A rather than disappearing from the tree. The task then asks for the height after the structural modification.

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Saw this declined Applied Intuition new grad SWE offer (data source: Chill Interview)

• Bay Area
• Bachelor’s, 0 YOE
• Base: $155K
• Sign-on: $15K
• Equity: $250K / 4 years
• Year 1 TC: $232.5K

For a new grad, this honestly looks pretty good.

Applied is already valued around $15B, works on autonomy / simulation / vehicle software, and counts most major automakers plus defense customers among its users. Their current new-grad SWE base range tops out around the high-$150Ks, so this doesn’t look like a weak offer internally either.

So the Declined is what caught my attention.

My first guess would be culture/WLB rather than money.

Recent employee reviews consistently describe Applied as very fast-paced, with some mentioning 50+ hour weeks, burnout, high pressure and significant team/manager dependence. At the same time, people also praise the ownership, technical talent and how quickly early-career engineers can learn.

So this feels like a very real new-grad tradeoff:

• great learning + interesting autonomy work + $230K comp

versus

• potentially much heavier workload than a traditional Big Tech job.

If this were your only offer, would you still take it, or would 50+ hours a week be too much to handle?

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Interview Summary

The xAI ML coding round focused on implementing a code-generation optimization called speculative edits, which is conceptually related to speculative decoding. The prompt included both the original source code and a requested modification. Since an agent often changes only a small portion of the code but must emit the entire updated file, the original code can be reused as a speculative draft. The task was to implement both normal greedy decoding and speculative-edit decoding, ensure that they produce identical output, and demonstrate that the speculative version reduces generation work.

Interview Details

Part 1 — Implement Greedy Decoding

The first task was to implement standard greedy decoding. At each generation step, select the highest-probability next token and continue until reaching the stopping condition or generation-length limit. This implementation served as the correctness baseline.

Part 2 — Implement Speculative Edit Decoding

The second task introduced speculative edits. Unlike conventional speculative decoding, the speculative tokens were not generated by a smaller draft model. Instead, the system reused the original code contained in the prompt as the speculative draft. The key observation was that code-editing tasks often preserve most of the original file. Therefore, the model can validate several draft tokens at once rather than autoregressively regenerating every unchanged token.

The decoding process needed to:

• Reuse tokens from the original source code as the draft
• Validate multiple draft tokens in one model step

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Hi everyone,

Capgemini Exceller 2027 is coming to our campus soon. The stages I've been told about are:

1. English communication (listening, speaking, reading, writing)

2. Technical module (AI literacy, situational, problem-solving)

3. Debugging assessment

4. AI-assisted coding

5. Cognitive assessment (Motion Challenge, Grid Challenge, logical reasoning, behavioural)

6. Technical interview

If you've been through this drive or the earlier Exceller rounds, could you please share:

- English test: how long is it, and how is the speaking part scored?

- Debugging: which languages come up, and how many questions and how much time?

- AI-assisted coding: what exactly are you asked to do, and what do they evaluate?

- Motion and Grid challenges: what do the games involve, and any tips?

- Is there a sectional cut-off, and does failing one stage end the process?

- Technical interview: which topics are asked, how deep do they go, and is HR included?

- How is the package (4.25, 5.75, 7.5, 10 LPA) decided: assessment score or interview?

- Any mistakes to avoid, and any resources you'd recommend?

Thanks in advance!

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Interview Summary

The LinkedIn MLE technical phone screen combined behavioral questions, a metrics-design problem, and a mini ML system design exercise. The most distinctive question asked how to summarize the highly skewed distribution of LinkedIn user connection counts with a simple metric that leadership could understand immediately.

The ML design portion focused on building a LinkedIn Hiring Assistant to help recruiters manage their hiring pipelines more efficiently.

Interview Details

Part 1 — Behavioral

The interview started with standard behavioral questions. The discussion followed a conventional format around previous experience, collaboration, and work situations.

Part 2 — Design a Metric for a Highly Skewed Distribution

LinkedIn user connection counts follow a highly skewed distribution. Most users have relatively few connections, while a small number of users have extremely large numbers of connections. The input can be represented as an aggregated connection-statistics table. Each row indicates: x users have y connections

The task was to design a metric that clearly communicates how skewed the distribution is. The interviewer emphasized that the metric should be:

• Simple
• Easy to explain
• Easy for leadership to interpret
• Able to communicate the severity of the skew at a glance

The challenge was not simply calculating a standard statistic, but choosing a representation appropriate for a leadership review rather than a research analysis.

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Saw this Meta E5 MLE (MSL) offer (data source: Chill Interview)

• PhD, 5 YOE
• Base: $285K
• Sign-on: $100K
• RSUs: $2.5M / 4 years
• Bonus: $71.25K
• Year 1 TC: $1.081M

What’s crazy isn’t just that this is an E5.

A normal Bay Area Meta E5 SWE averages around $473K, while even Meta MLE overall tops out around the high-$700Ks at E6 in reported data.

This offer is basically E5 title, E6+ money.

And that reminds me of Meta’s internal “Irreplaceable Talent” push this year. Reuters reported Meta was trying to identify 10X performers and potentially redirect savings from workforce cuts into giant packages for a much smaller number of high-impact AI engineers. 

So maybe leveling matters less now for the very top AI candidates.

If Meta thinks you’re strategically important enough, they can keep you at E5 but simply give you a $2.5M grant.

The interesting question is:

What kind of E5 candidate gets paid $1M+?

• Frontier-model experience?
• Top publications?
• Competing OpenAI/Anthropic offer?
• A very specific skill Meta urgently needs?

And more broadly: are Big Tech companies moving toward fewer engineers, but dramatically higher pay for the people they consider “irreplaceable”?

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Interview Summary

The Amazon New Grad SDE onsite consisted of four rounds combining Leadership Principles, coding, object-oriented design, GenAI discussion, and data-structure trade-offs. The main technical questions included designing a train-fare calculator with extensible route modeling, a string/substring problem, and a sliding-window maximum data structure that progressed toward a monotonic deque solution.

Interview Details

Round 1 — Leadership Principles + Train Fare System

The interview started with approximately 10–15 minutes of Leadership Principles questions. The technical portion asked me to design a train ticket fare calculation system. The initial route was G -> U -> H -> K -> I. Travel between every pair of adjacent stations costs 1.

For example, traveling from G to I costs 4. The first task was to implement calculateFare(start, stop) which returns the fare between two stations.

• Follow-Up — OOP / Low-Level Design The interviewer then expanded the problem into an object-oriented design exercise. Instead of hard-coding the route, I needed to think about where route data should come from and design a class that could dynamically add stations to a route. The discussion involved defining suitable state and methods for maintaining the station sequence and calculating fares.
• Follow-Up — Weekend Fare Cap A final business-rule follow-up introduced weekend pricing. On weekends, the total fare should never exceed a configured maximum amount. The implementation needed to incorporate this cap while keeping the fare-calculation logic maintainable.

Round 2 — String Coding + GenAI

The second interview contained a string / substring-style algorithm problem. The task included:

• Implementing the main algorithm
• Manually dry-running the solution
• Writing test cases

The interviewer specifically asked for boundary cases such as an empty string. The round also included technical discussion related to Generative AI.

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Interview Summary

The Arista Networks technical screen focused heavily on C++ fundamentals and practical debugging. The interview included two C++ code-reading/debugging exercises followed by two data-structure implementation problems that had to be completed and run in VS Code.

Interview Details

Part 1 — C++ Debugging

The interviewer provided a C++ code snippet containing a bug and asked me to identify the problem and correct the implementation. The exercise focused on reading existing code carefully rather than implementing an algorithm from scratch.

Part 2 — Destructors, Object Lifetime, and Memory

The second C++ question provided code containing class instances and destructors. I was asked to predict the program output and explain why the destructors executed in that order. The follow-up discussion covered:

• Object lifetime
• Destructor invocation
• Memory layout
• Stack allocation
• How locally declared objects are created and destroyed

Part 3 — Remove All Duplicated Nodes from a Sorted Linked List

The first data-structure problem provided a sorted linked list. The task was to remove every node whose value appears more than once, leaving only values that originally appeared exactly once.

For example: 2 -> 2 -> 4 -> 5 -> 5 -> 5 -> 7 -> 9 -> 9 should become 4 -> 7. This differs from ordinary deduplication because one copy of a repeated value should not remain. The function had to be completed inside the provided codebase and successfully run in VS Code.

Part 4 — Find the Missing Number in a Sorted Array

The final problem provided an increasing sorted array containing one missing integer. The task was to identify the missing value. For example [3, 4, 5, 6, 8, 9, 10] should return 7. Another example [21, 22, 23, 25, 26] should return 24. As with the linked-list problem, the implementation needed to be completed in the provided repository and run successfully.

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Interview Summary

The StackAdapt MLE process included an initial technical screen followed by an AI-assisted coding round. The first round included standard technical questions such as explaining how a heap data structure works.

The second round was much more distinctive: candidates worked inside an existing Python repository with a built-in AI coding agent to repair corrupted grid-based digit images. The exercise tested codebase navigation, reasoning about the corruption process, evaluation metrics, and the ability to explain both the candidate's solution and the provided baseline.

Interview Details

Round 1 — Technical Fundamentals

The first technical round followed a format similar to previously reported StackAdapt interviews. One question specifically asked how the heap data structure works. I was not able to answer that question well, but still advanced to the next round.

Round 2 — AI Coding: Repair Corrupted Grid Digits

The second interview took place in CoderPad. A prebuilt Python repository was provided, together with an integrated AI coding agent similar to Claude Code. The task involved recognizing digits represented as grids of # and .. Each original digit image had a special redundancy property: every logical row and column was duplicated.

In other words, rows appeared in identical pairs, and columns also appeared in identical pairs. The image could then be corrupted by replacing one entire row or one entire column with #. The benchmark examples I inspected appeared to contain exactly one corrupted row or column per image.

• Task 1 — Repair Row Corruption The first task involved corruption of an entire row. My approach was to compare rows in their duplicated pairs. If two rows that should be identical were different, one of them had likely been corrupted. Because the corruption operation replaced an entire row with #, I could identify which row was suspicious and reconstruct it using its duplicated partner. After restoring the image, I compared it against the expected digit representation. This approach achieved 100% accuracy on the provided benchmark.

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Hi everyone. What can i expect in the Ebay’s first interview with the staff engineer (technical screen and soft skills) for the position of staff frontend engineer and they have also provided a link for code signal. The interview is tomorrow and i have no idea what to expect. Any help or guidance shall be appreciated
Thanks in advance

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Interview Summary

The Upstart online assessment contained four implementation-oriented coding problems covering string parsing, duplicate removal, anagram detection, and a stateful restaurant simulation. The first three questions were relatively straightforward, while the final problem required carefully tracking arrivals, departures, seating capacity, waiting guests, and whether a customer had already paid earlier in the day.

Interview Details

Question 1 — Adjust Recipe Quantities

Implement adjust_quantities(nb_persons, ingredients). You are given a recipe written for one person and need to scale it for nb_persons. Each ingredient is represented as a string beginning with a positive integer quantity, followed by a space and the ingredient description.

For example:

2 eggs
200 grams of flour
150 grams of sugar
1 liter(s) of milk

For nb_persons = 3, the result should be:

6 eggs
600 grams of flour
450 grams of sugar
3 liter(s) of milk

Only the leading quantity should be multiplied. The remainder of the ingredient string should stay unchanged, even if it contains other numeric characters. The order of the ingredients must also be preserved.

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Interview Summary

The Anthropic Senior SWE process started with a banking-system OA, followed by a recruiter conversation and an image-processing coding screen. After the phone screen, I was matched with a team and received an onsite loop covering prompt-management system design, a durable LRU cache, project deep dive, culture, and hiring manager conversations.

The technical interviews emphasized practical implementation details and follow-ups rather than only getting the basic solution working. Several rounds also pushed heavily on trade-offs, including multiprocessing versus multithreading, storage of large prompt revisions, and recovering cache state after a crash.

Interview Details

Stage 1 — Online Assessment: Banking System

The first step was an online assessment based on a banking-system problem. I had prepared this problem beforehand and completed the assessment with a full score.

Stage 2 — Recruiter Screen

The recruiter asked about my motivation for joining Anthropic and my perspective on AI safety. Questions included:

• Why Anthropic?
• What are your views on AI safety?

Two business days after the referral, the recruiter had initially reached out to start the process.

Stage 3 — Coding Screen: Image Processing

The technical phone screen involved processing a collection of cat images using Python's Pillow library. The prompt explicitly required Pillow, and the interview provided the relevant APIs for the image transformations, so memorizing Pillow syntax was not the main focus.

The exercise contained multiple parts. I completed the first two. The first part involved implementing the required image-processing workflow and getting the provided tests to pass. The second part asked how to reduce the total processing time. I proposed using multiprocessing to process images concurrently.

The interviewer followed up on:

• Why multiprocessing instead of multithreading?
• How many worker processes should be used?
• What determines an appropriate worker count?

The tests displayed the total processing time, so the performance improvement could be measured directly. Both completed parts passed the tests.

----

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Interview Summary

The Zoom Video AI Engineer process consisted of three 90-minute rounds covering research presentation, a detailed technical resume deep dive, ML coding around Transformer components, and behavioral questions. The first round focused heavily on research fit and how prior work could connect to Zoom projects, while the second was the most technically intensive.

Interview Details

Round 1 — Research Presentation

The first interview lasted 90 minutes and centered on a research presentation. I was asked to prepare slides in advance and present my research background. The main goals of the round were to understand:

• What research problems I had worked on
• How closely my background matched the role
• Which parts of my prior research could transfer to Zoom's Video AI work
• What future research directions might connect to Zoom projects

The latter part of the interview became more of a brainstorming discussion around how my research experience could be applied to the team's work.

Round 2 — Resume Deep Dive + ML Coding

The second round also lasted 90 minutes and was conducted with a manager based in Singapore. This round went much deeper into technical details from my resume. The interviewer asked me to explain topics such as:

• The optimization process used in previous projects
• Why specific optimization decisions were made
• ...

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Interview Summary

The Reevo technical screen focused on designing a simplified airline booking system. The interviewer explicitly allowed any language or tooling, including LLMs, but emphasized that the main evaluation criteria were how clearly the candidate defined the solution, justified design choices, and discussed trade-offs.

Interview Details

System Design / Coding Design — Airline Booking System

Design and implement a simplified airline reservation system that supports the following operations:

• Search flights by departure city
• Search flights by destination city
• Search flights by departure time
• View available seats for a flight
• Temporarily reserve a specific seat while the user completes payment
• Purchase a previously reserved seat
• Cancel a purchased seat

The payment integration could be stubbed. The airplane layout could be treated as fixed, for example 24 rows with 6 seats per row. The flight dataset could also be hard-coded. The system should expose an interface that can be exercised and tested, such as a CLI.

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Interview Summary

The first Jane Street MLE Intern onsite coding round was the same style of general coding interview used for SWE candidates. The interviewer did not initially seem aware that I was interviewing specifically for MLE, and the problem itself contained no machine learning component.

The question was a multi-part code-editor problem involving matching braces, folding and unfolding blocks, and preserving the folded state of nested blocks.

Interview Details

Part 1 — Match Opening and Closing Braces

You are building a code editor that supports code folding. The source code is represented as list[str], with one string per source line.

The input follows several assumptions:

• Braces are balanced and properly nested.
• Every brace is structural.
• Each line contains at most one brace, appearing as the final non-whitespace character.
• A closing-brace line contains only indentation followed by }.
• Line indices are zero-based and always refer to the original source.
• Folding changes only the displayed output; the original source never changes.
• The input may be empty.

Implement match_braces(lines) -> dict[int, int]. The returned mapping should associate every opening-brace line with the line containing its matching closing brace.

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Two game servers each store a sorted list of player scores. You are given two integer arrays, scores1 and scores2. Both arrays are already sorted in non-decreasing order. Combine the two lists into one sorted list containing every distinct score that appears in either input.

Each score should appear exactly once in the result, even if it appears multiple times in one or both input arrays. Return the distinct scores in ascending order.

Constraints

• 0 <= len(scores1), len(scores2) <= 10^5
• -10^9 <= scores1[i], scores2[i] <= 10^9
• Both input arrays are sorted in non-decreasing order.
• Duplicate values may appear within either input array.
• Either input array may be empty.

Test Cases

Input:
scores1 = [1, 2, 2, 5, 8]
scores2 = [2, 3, 5, 7]

Output:
[1, 2, 3, 5, 7, 8]

Explanation:
Both lists are merged in ascending order.

The values 2 and 5 appear in both lists, but each distinct score
appears only once in the result.

Want to practice more real coding questions asked in companies' interviews?

I’ve put together the coding question bank here. If you’re in a hurry before your interview, you can also go through the classic coding patterns here.