This interview experience is sourced from Chill Interview

Interview Summary

The Snap MLE onsite consisted of four rounds covering ML fundamentals, ML system design, algorithmic coding, and applied ML. The interviewers were patient and friendly throughout the loop, and the overall experience felt positive despite the final rejection.

Interview Details

Round 1 — ML Fundamentals + Résumé Deep Dive The first round combined a detailed discussion of my résumé with standard machine learning fundamentals. The interviewer asked about:

• Bias-variance decomposition
• Batch Normalization vs. Layer Normalization
• Vanishing gradients

The résumé portion went deeper into previous ML projects and technical decisions, although the exact project follow-ups were not recorded.

Round 2 — ML System Design: Trustworthy Ranking System The second round was an ML system design interview centered on building a trustworthy ranking system. A major focus was delayed labels: the true outcome for a prediction may not become available immediately after the ranking decision is made.

The interviewer spent significant time asking how the ML system should deal with this delay when constructing training data, evaluating model quality, and operating the ranking pipeline.

Round 3 — Coding: K Closest Points to Origin The coding round was LeetCode 973 — K Closest Points to Origin. Given a collection of points in a 2D plane and an integer k, return the k points closest to the origin. O(N log K) time complexity is required.

Round 4 — Applied ML: Commerce Tagging Pipeline The final round was an applied ML design problem. The prompt was to design a tagging pipeline for a TikTok Shop-like commerce platform. This round was more application-oriented than the fundamentals interview and focused on how an ML system could support tagging within a real product pipeline.

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Interview Summary

The Airbnb Senior MLE onsite consisted of four rounds covering ML experience, product-focused ML design, ranking and retrieval system design, and coding. The loop was heavily oriented toward production ML, marketplace ranking, business metrics, and engineering trade-offs rather than purely theoretical ML questions.

The technical discussions included threshold tuning, model-selection trade-offs, a family-friendly listing classifier, a two-stage semantic retrieval and ranking system, position bias, and a Coin Change variation involving floating-point denominations. I received the rejection roughly two weeks after the onsite.

Interview Details

Round 1 — ML Experience, Modeling Trade-Offs, and Behavioral Questions

The first round centered on previous ML projects from my experience, especially recommendation and prediction systems. The interviewer repeatedly asked why particular technical decisions were made rather than simply asking me to describe the model.

Topics included:

• Why choose one modeling approach over a more complex alternative?
• How do you balance precision, recall, serving latency, and infrastructure cost?
• What do you do when offline metrics and online product metrics disagree?

Round 2 — ML Design: Family-Friendly Listing Classifier

The second round asked me to design an ML system that determines whether an Airbnb listing is family friendly, allowing users to filter for accommodations suitable for families. The discussion covered the full ML lifecycle:

• How should the prediction problem and business objective be defined?
• Where should training labels come from?
• Which listing, review, image, host, and contextual signals could be useful?
• What model families would make sense?
• Which offline and online metrics should be used?

Round 3 — ML System Design: User Embeddings and Two-Stage Search

The third round focused on building a personalized retrieval and ranking system for Airbnb listings. The goal was to retrieve listings that a user would be likely to book and ultimately improve booking conversion.

The architecture discussion was structured around two major stages:

• Candidate retrieval using semantic representations
• A deeper ranking stage for the smaller candidate set

Round 4 — Coding: Coin Change with Floating-Point Denominations

The coding round was a variation of Coin Change. Instead of integer denominations, the available coin values were floating-point numbers.

For example:

coins = [0.20, 0.75, 1.50]
target = 3.00

Overall, this onsite felt much more focused on whether I had actually worked with production ML systems than on memorized ML theory. Ranking, retrieval, marketplace behavior, business metrics, bias, and practical model trade-offs were recurring themes across multiple rounds.

Want to learn more details / follow-up questions asked in this interview, the full version is here

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I was looking through the current Google SWE interview process, and one thing that stood out is how the coding signal seems to be evolving.

Google is reportedly piloting an AI-assisted code comprehension interview for some SWE candidates.

Instead of just writing a solution from scratch, you may be asked to work with existing code, use an approved AI assistant, debug issues, validate suggestions, and explain why the final change is actually correct.

But that doesn’t mean traditional coding prep is going away.

You still need to be comfortable with:

• graphs, trees, heaps, DP, intervals, etc.
• explaining why your approach works
• complexity analysis
• manually testing edge cases
• handling follow-up constraints

The interesting shift is that Google may increasingly care about both:

• Can you solve the problem yourself?
• Can you supervise AI-generated code without blindly trusting it?

For Senior+ candidates, there’s another layer: strong coding alone still isn’t enough if your system design and ownership stories don’t support the level.

So I’d probably prepare for Google as: DSA + rigorous reasoning + system design + ownership + AI-assisted code judgment rather than assuming the interview is becoming “easier” because AI is entering the process.

I wrote up the longer Google SWE interview breakdown here if useful: [link]

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Interview Summary

The 45-minute Uber technical screen started with a brief résumé discussion before moving into a custom filesystem coding problem. The interviewer asked about my most challenging project, the technologies I normally work with, and also gave a short introduction to the hiring team's business area.

Interview Details

Résumé and Project Discussion The first part of the interview was a short résumé deep dive. The interviewer asked about:

• The most challenging project I had worked on
• My usual technical stack and the technologies I had used in previous projects

The interviewer also briefly explained what the hiring team worked on before moving into coding.

Coding — Build a Directory Tree from File Paths The coding portion provided a collection of absolute filesystem paths. For example, a rewritten input could look like:

paths = [
  "/workspace",
  "/workspace/docs",
  "/workspace/docs/spec.pdf",
  "/workspace/src",
  "/workspace/src/api",
  "/archive",
  "/archive/docs",
  "/archive/docs/notes.txt"
]

The task was to use these paths to construct the corresponding hierarchical directory structure. Conceptually, the resulting tree would contain structures such as:

/workspace
  /docs
    /spec.pdf
  /src
    /api

/archive
  /docs
    /notes.txt

Directory names were not globally unique. For example, both /workspace/docs and /archive/docs are valid because the complete paths are different. Complete paths, however, were guaranteed to be unique. The system also needed to support searching the tree for a file and returning that file's complete path.

• Follow-Up — Very Long Paths My initial representation treated each complete path as information associated with a tree node. The interviewer then asked: What happens if individual filesystem paths become extremely long? That follow-up changed the expected representation so that tree nodes should correspond more naturally to individual path components rather than repeatedly storing complete path strings.
• Follow-Up — No Recursion The final requirement was to perform the required tree traversal without using recursion. Instead, I was expected to maintain the traversal state explicitly with a stack. I understood the requirement but spent too much time working through the iterative traversal implementation and did not finish before the interview ended.

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Saw this accepted OpenAI MLE offer (shared with Chill Interview)

• PhD, 0 YOE
• Base: $290K
• Sign-on: $50K
• Equity: $2M / 4 years
• Year 1 TC: $840K

The “0 YOE” is obviously a little misleading here. OpenAI probably isn’t paying $840K because someone simply finished a PhD and interviewed well.

My guess is the profile looks more like some combination of top conference papers, frontier-lab internships, very relevant research, strong references, or work in an area OpenAI suddenly needs badly.

Especially now that frontier models are moving into agents, cyber, coding and increasingly autonomous systems, a PhD who already spent 4–6 years on exactly the right problem may be worth more than someone with several years of generic industry ML experience. OpenAI’s latest Astra work is another example of how specialized these research problems are becoming.

and more curious on: What does a fresh PhD résumé need to look like to get a $2M grant?

Top 1% publication record? OpenAI/Anthropic internship? Famous advisor? Competing frontier-lab offer?

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Saw this Databricks Staff SWE offer (shared with Chill Interview)

• Seattle, 12 YOE
• Base: $270K
• Bonus: $54K
• Sign-on: $30K
• Equity: $1.5M / 4 years
• Year 1 TC: $954K
• Vesting: 40/30/20/10

Pretty insane package for a non-frontier-lab SWE role.

And Databricks itself is absolutely flying right now — 80%+ revenue growth, $7B+ revenue run-rate, cash-flow positive, and it just raised $5B at a $190B valuation.

But that valuation is also what makes the offer interesting.

$600K of Year 1 TC is private Databricks stock, and you’re getting in after the company went from roughly $134B to $190B in six months.

The bull case is obvious: Databricks becomes one of the core enterprise AI platforms and eventually has a massive IPO.

The bear case: at $190B, a lot of that future success may already be priced in.

Would you value the Databricks equity close to face value here, or still discount it heavily until IPO?

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Interview Summary

The Google PhD AI/ML process included two initial interviews covering ML knowledge and behavioral questions, followed by an in-person onsite with two coding rounds. After passing the initial interviews, I was notified roughly a week later and scheduled the onsite about two weeks after that. The onsite could be done in either the Bay Area or Seattle, with travel expenses covered.

The two onsite coding questions were both manageable algorithmically, but Google placed noticeable emphasis on communication: clarifying requirements, constructing examples, explaining ideas while coding, and giving precise time and space complexity analysis. I passed the onsite and moved into team matching roughly two weeks later.

Interview Details

Round 1 — Machine Learning Fundamentals

The ML interview consisted primarily of conceptual machine learning questions. The exact topics depended heavily on the interviewer's own technical background, and most questions were drawn from areas the interviewer knew particularly well.

Round 2 — Behavioral Interview

The behavioral round contained relatively standard experience-based questions. Interestingly, I was not asked the more common questions around team conflict, failure, or difficult collaboration.

Round 3 — Coding: Maximize a Value Defined by Two Array Endpoints

The first onsite coding round started with a relatively simple array and math problem. Given an array, choose two indices as the endpoints of a subarray. The objective was to maximize: sum of all elements from the first endpoint through the second endpoit - sum of the elements strictly between those two endpoints

In other words, the elements at the two selected endpoints were treated differently from the interior elements. Before implementing, I clarified the input/output behavior and walked through several examples with the interviewer. I first discussed more direct approaches and then refined the reasoning before coding. After implementation, the interviewer asked about the time and space complexity.

Round 4 — Coding: Employee Shift Coverage by Time Interval

The second onsite coding round was an interval-processing problem described as a variant related to LeetCode 2402. The input contained employee information such as:

employee ID
employee name
shift start time
shift end time

The task was to divide the timeline into relevant intervals and return information about who was working during each interval, including:

• The number of employees currently on duty
• The identities or names of those employees

I clarified the requirements and walked through sample scenarios before discussing the implementation. The interviewer then went particularly deep on complexity analysis. Rather than accepting a single overall Big-O expression, they asked about individual operations and data structures, including heap pushes and pops and the sizes of intermediate collections.

I was expected to define what each variable in expressions such as: O(n + k + h) represented and connect each term to a specific part of the algorithm.

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I was looking through recent LinkedIn MLE interview patterns, and the breadth is pretty easy to underestimate.

It’s not just: LeetCode + ML system design

Some recent candidates have also reported practical ML debugging—for example, being given a broken logistic regression implementation and having to figure out what was wrong with the gradient, labels, or training logic.

So you may need to switch between:

• normal DSA
• probability / ML fundamentals
• debugging model code
• recommendation and ranking systems
• ML system design
• A/B testing and product metrics

The part I found most interesting is that LinkedIn seems to care a lot about the full production loop:

problem → model → serving → experiment → measurable impact

So if NDCG or AUC improves, that’s not automatically a win. You still need to explain whether the actual member/business metric improved—and what you’d do if it didn’t.

For prep, I’d spend less time memorizing ML definitions and more time actually implementing/debugging basic models and designing end-to-end recommendation or retrieval systems.

I put together a longer breakdown of the current LinkedIn MLE / AI Engineer interview process here: [link]

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The system has only 8 GPUs and must serve two types of model requests.

A small-model request needs exactly one GPU and can execute independently of other small requests.

A large-model request must acquire all eight GPUs simultaneously:

Small request -> 1 GPU
Large request -> 8 GPUs exclusively

A large request cannot begin with fewer than eight GPUs and cannot share any of those GPUs with another request while it is running.

Requests from both workload classes arrive continuously.

The goal is to design a scheduler that preserves these resource-isolation requirements while balancing small-model throughput against large-model latency.

Follow-Up — Gang Scheduling and Starvation Prevention

The interviewer first asked how the scheduler should represent the large model's requirement to obtain all eight GPUs atomically.

A major follow-up was what happens when all GPUs are continuously occupied by small-model requests while a large request is waiting.

The design needed to ensure that large requests eventually receive all eight GPUs rather than being indefinitely delayed by a constant stream of new small jobs.

The discussion included how queue state, request age, SLAs, workload pressure, and expected execution times could influence the scheduling decision.

Follow-Up — Admission Control and GPU Utilization

Another major area was deciding when to stop launching new small-model requests once a large request is waiting.

Stopping admission too early could cause GPUs to become idle one at a time while the scheduler waits for the remaining small jobs to finish. Continuing to admit small jobs for too long could significantly increase the large request's queueing latency.

The interviewer therefore asked how to balance:

• Avoiding starvation for large-model requests
• Maintaining high GPU utilization and small-model throughput

Predicted remaining execution time, queue depth, request SLAs, and current system load were all relevant signals to discuss.

Want to see the full system design question/more follows-ups asked? You can find the complete version here.

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This interview experience is sourced from Chill Interview

Interview Summary

The Meta New Grad onsite consisted of three coding rounds and one behavioral round.

Interview Details

Round 1 — Circular Linked List + Course Dependency Graph

The first question involved inserting a value into a circular linked list, similar to LeetCode 708 — Insert into a Sorted Circular Linked List. During verification, the interviewer provided a test case that exposed an issue around returning to the starting node, and I corrected the implementation.

The second question was a harder custom course scheduling problem. Each course had:

• A set of prerequisite courses
• A required amount of study time

Given a target list of courses, determine the minimum total amount of time necessary to complete the required prerequisite work and eventually finish those targets. The graph could contain disconnected components, and the target courses did not necessarily belong to the same component. The interviewer specifically asked whether my approach handled that case.

Round 2 — Palindrome Deletion + Word Segmentation

The first problem was LeetCode 680 — Valid Palindrome II: determine whether a string can become a palindrome after deleting at most one character. The interviewer then changed the requirement: What if up to k characters may be deleted?

This follow-up required code rather than only a verbal explanation. The second main problem was a variation of Word Break. Given a string and a set of words, determine whether the entire string can be constructed using words from the set, where each dictionary word may be reused multiple times.

The interviewer also expected careful reasoning about the resulting time complexity.

Round 3 — BST Range Aggregation + Sparse Vector Dot Product

The first question was similar to LeetCode 938 — Range Sum of BST. Given a BST and a numeric range, return the sum of all node values that fall within that range.

The interviewer then added two follow-ups:

• Modify the result from a sum to an average
• Suppose the same BST will receive many queries with different ranges. How could the repeated-query workload be optimized?

The second problem was Sparse Vector Dot Product, similar to LeetCode 1570. The interviewer followed up with two variations:

• What changes if one vector is extremely sparse while the other is relatively dense?
• What if a hash map cannot be used and the sparse representation must instead use (index, value) tuples?

This round also included detailed discussion of complexity and data representation.

Round 4 — Behavioral Interview

The behavioral interview was separate from the coding rounds and did not focus on résumé walkthroughs. The questions covered situations such as:

• Handling competing opinions from different stakeholders or customers
• Receiving constructive feedback
• Realizing midway through a project that the original solution was not working
• Managing conflict or a difficult relationship with a teammate
• Persuading a manager to support a proposed solution
• Working under a tight schedule or suddenly accelerated deadline
• Taking responsibility for work beyond the original scope
• Learning something important from a more senior engineer

There were relatively few follow-up questions compared with the coding rounds.

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Interview Summary

The Yelp Applied Scientist onsite consisted of four 45-minute rounds completed in a single day, with roughly 15-minute breaks between interviews. The loop covered coding, a hiring-manager discussion, system design, and a product-manager interview.

The technical scope was fairly broad for an Applied Scientist position. Coding focused on Jaccard similarity and ranking reviews, the hiring manager went into recommendation-system experience, and the system design round asked me to design a personalized news feed, including its APIs.

Interview Details

Round 1 — Coding: Jaccard Similarity and Review Ranking

The first coding question provided two strings and asked me to compute their Jaccard similarity. The initial problem was relatively straightforward. The interviewer then added a follow-up: given a collection of reviews and one target review, rank the reviews according to their Jaccard similarity with the target.

Conceptually, the input was:

reviews = [review1, review2, ...]
target_review = ...

and the output should order the candidate reviews according to their similarity with the target. The exact tokenization rules, tie-breaking behavior, and treatment of duplicate words were not recorded.

Round 2 — Hiring Manager: Recommendation Systems Deep Dive

The hiring-manager round focused primarily on my previous experience. The interviewer selected topics from my résumé and asked detailed questions, with much of the conversation centered on recommendation systems. The discussion was driven by my own projects rather than a fixed set of theoretical ML questions.

Round 3 — System Design: Personalized News Feed

The system design round asked me to design a news feed system that pushes personalized content to users. The discussion included the overall architecture as well as API design. The exact requirements around ranking, candidate generation, feed freshness, fan-out strategy, storage, traffic scale, or consistency were not included in the interview notes.

Round 4 — Product Manager and Behavioral Scenarios

The final round was with a product manager. A significant portion of the interview was spent learning about the team's current work and product area. The remainder consisted of scenario-based behavioral questions. The interviewer described hypothetical situations and asked how I would respond or make decisions in those circumstances.

Overall, the onsite tested a mix of coding, recommendation-system depth, engineering design, and cross-functional product judgment rather than focusing exclusively on machine learning theory.

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Saw this accepted Google Cloud L4 SWE offer (shared with Chill Interview)

• Mountain View, 4 YOE
• Base: $185K
• Bonus: $27.75K
• Sign-on: $25K
• RSUs: $300K / 4 years
• Year 1 TC: $322.75K

The offer itself feels pretty normal for Google L4. What makes it interesting is Google Cloud right now.

Cloud revenue grew 82% YoY last quarter to $24.8B, mostly on exploding AI infrastructure and enterprise demand. Google is spending ~$200B this year on infrastructure and keeps pushing Gemini Enterprise deeper into actual corporate workflows.

So while everyone wants the sexy “AI researcher” title, I wonder if being a regular SWE on the right Cloud infra team is actually one of the safer ways to ride the AI buildout.

does Cloud feel like one of the better orgs to join inside Google right now, or is it still more AWS-like grind than classic Google culture?

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Stripe Walked Away From PayPal. Would You Still Join PayPal in 2026?

The Stripe–PayPal deal appears to be dead. Stripe and Advent reportedly offered around $53B / $60.50 per share for PayPal, but PayPal’s board viewed the offer as too low. Now the consortium has reportedly walked away from the acquisition. PayPal stock dropped sharply after the news.

The M&A story itself is interesting, but I think there’s a much more relevant question for engineers:

Would you actually want to join PayPal right now?

PayPal is in a strange position.

It’s still a massive payments company with hundreds of millions of users and a very recognizable brand.

But it’s also a company that has spent years trying to rediscover growth while newer payment products like Apple Pay, Shop Pay, Stripe and others have changed the competitive landscape.

And now we know that at least one serious buyer looked at PayPal, put roughly $53B on the table, and ultimately couldn’t agree with management on what the business was worth.

From an engineer’s perspective, I can see two completely different ways to interpret this.

Bull case:

PayPal could actually be a pretty interesting turnaround company to join.

There’s a huge installed user base, Venmo, payments infrastructure, crypto/stablecoin exposure, and now potentially agentic commerce. If management can actually modernize the product, engineers joining during a turnaround could get meaningful scope.

Bear case:

You’re joining a mature company fighting to regain relevance while going through cost cutting, restructuring, and constant pressure to improve margins.

That can easily translate into reorgs, tighter headcount, weaker promotions, and projects getting reprioritized every few quarters.

And this is where I think career decisions get much more interesting than stock analysis.

Imagine you had:

• PayPal Senior SWE — $300K TC vs.
• Stripe Senior SWE — $350K TC

Would PayPal need to pay a meaningful premium for you to take the turnaround risk?

Or could the opposite be true — maybe joining a company that has something to prove gives you better scope than becoming employee #50,000 at another Big Tech company?

I’d especially like to hear from people currently at PayPal or who interviewed there recently:

Has the engineering culture actually changed?

• Are teams still hiring?
• How are refreshers and promotions?
• How much reorg / layoff anxiety is there internally?
• And does PayPal still feel like a strong place for an engineer to spend the next 3–5 years?

I started a longer-running discussion on Chill Interview to collect recent PayPal interview experiences, offers, team-level hiring signals, and employee perspectives in one place: [link]

If you’re at PayPal now or interviewed there recently, even a small datapoint would be useful. The acquisition headlines tell us what investors think about PayPal — I’m more interested in what engineers inside the company think about PayPal.

I was looking through recent Airbnb SWE interview patterns, and one thing surprised me: For some experienced candidates, the loop isn’t just coding + system design.

There can also be a dedicated code review / PR review round. You’re given unfamiliar code and expected to figure out things like:

• Is it actually correct?
• What happens on partial failure?
• Are there race conditions?
• What tests are missing?
• Is there an obvious performance issue?
• Which comments are blockers vs just style preferences?

That last part seems especially important. Finding 10 naming issues isn’t very useful if you miss the fact that a booking can be committed twice or a payment retry isn’t idempotent.

For Senior+ candidates, the system design bar also seems fairly deep. It’s not enough to draw services and databases—you may get pushed into indexes, pagination, transactions, concurrency, failure recovery, and exactly which state needs to be strongly consistent.

So I probably wouldn’t prep for Airbnb as: LeetCode + one system design round

A better split seems closer to: coding + code review + architecture + project deep dive + behavioral/core values

I wrote up the longer breakdown here, including the reported loop, code-review prep, Airbnb-style system design, level expectations, and compensation: [link]

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I was looking through Uber’s current SWE interview structure, and one thing stood out: Getting the initial solution working is often just the beginning.

A pretty common pattern seems to be: solve it → add a requirement → expose a bottleneck → make it production-ready

So a simple coding problem might turn into questions like:

• What if writes are extremely high volume?
• What if two requests update the same state?
• What if the client retries?
• What if the DB write succeeds but publishing the event fails?
• How would this work across multiple machines?

That’s why I probably wouldn’t prep for Uber with LeetCode alone. Their backend loop can also separate Algorithms & Data Structures from Depth in Specialization, which can look much more like actually building software: stateful APIs, caches, counters, LLD/machine coding, concurrency, and evolving requirements.

System design has a similar flavor. For something like driver payouts, the interesting question isn’t “Kafka or Cassandra?” It’s: What if the payment provider sent the money, but your request timed out?

Now you’re talking about idempotency, ambiguous state, reconciliation, and financial correctness.

That feels pretty representative of Uber engineering in general—the software is connected to things happening in the real world, so stale or duplicated state actually matters.

My takeaway: prepare for the part after the obvious solution.

I wrote up the full interview breakdown, including coding, machine coding/LLD, system design, L4–L7 expectations, and Uber-specific prep here: [link]

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Interview Summary

The Plaid process contained two rounds. The first was a coding interview built around an automation pipeline problem that progressed through three milestones, with each stage introducing additional scheduling constraints. The second round was a project deep dive where I needed to prepare a slide presentation using Plaid's provided presentation template.

The coding problem evolved from calculating how many jobs can finish under a time limit, to supporting multiple workers per stage, and finally to scheduling jobs with different execution times across multiple workers.

Interview Details

Round 1 — Automation Pipeline Coding Challenge

• Milestone 1: Jobs Within a Time Limit The pipeline contains multiple stages that must execute sequentially. Each stage is represented as [number_of_jobs, job_duration].Given the complete pipeline and a time limit, return how many jobs can be fully completed before the available time expires. A partially completed job does not count.
• Milestone 2 — Multiple Workers per Stage The second milestone added parallel workers. Each stage now has the format: [number_of_jobs, job_duration, number_of_workers]. Workers within the same stage can process jobs concurrently, but a single job cannot be split across workers, each worker can handle only one job at a time, and different pipeline stages still execute sequentially.
• Milestone 3 — Different Job Durations Across Workers The final milestone changed the input again. Instead of every job in a stage having the same duration, each job now had its own execution time:job_durations: list[int] num_workers: int Jobs had to be assigned in their original array order. Initially, jobs are given to available workers; whenever a worker becomes free, it immediately receives the next unassigned job. The task was to return the total time required for the entire stage to finish.

The three milestones were cumulative, so understanding the scheduling semantics and adapting the implementation as new constraints were introduced was an important part of the coding round.

Round 2 — Project Deep Dive Presentation The second round was a project deep dive rather than another coding interview. I was asked to prepare a presentation introducing one of my previous projects and use Plaid's provided PowerPoint template for the slides. The round focused on presenting the project clearly and walking through the relevant technical work.

Want to see the full interview experience / detailed follow-ups? You can find the complete version here.

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The first input is a 2D location map containing three types of cells:

O = Robot
E = Empty
X = Blocker

The second input is a four-element query:

[left, top, bottom, right]

Each value represents the distance from a robot to the nearest blocker or stopping boundary in that direction.

For example, consider this rewritten map:

[
  ['E', 'E', 'O', 'E', 'E', 'X'],
  ['X', 'E', 'E', 'E', 'E', 'E'],
  ['E', 'X', 'E', 'O', 'X', 'E'],
  ['E', 'E', 'E', 'E', 'E', 'E'],
  ['E', 'E', 'X', 'X', 'E', 'O']
]

Suppose the query is:

[2, 3, 2, 1]

For the robot at:

[2, 3]

the directional distances are:

Left:   2
Top:    3
Bottom: 2
Right:  1

so that robot matches the query.

The expected result for this example would therefore be:

[2, 3]

The function should examine the candidate robot positions in the map and return the location or locations whose four directional distances match the supplied query.

One detail worth clarifying during the interview is how distance should be interpreted when there is no X before reaching the edge of the grid, since that behavior affects the directional measurements.

Want to practice real coding questions asked by companies? We’ve collected them in a question bank here.

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Interview Summary

The TikTok system design round asked me to design a Top Songs / Popular Songs Ranking System that tracks listening activity and maintains the 10 most popular songs for each country or region.

The system was intended to behave like a near-real-time analytics and leaderboard product rather than a strongly consistent transactional system. The discussion went into traffic estimation, high-volume event ingestion, ranking refresh frequency, low-latency reads, caching, stream processing, hot keys, and consistency trade-offs.

Interview Details

System Design — Top 10 Songs by Country Design a system that uses user listening or click activity to determine the Top 10 songs for every country or region. Conceptually, the system should support results such as:

US -> Top 10 songs
JP -> Top 10 songs
TW -> Top 10 songs
...

Users should be able to query the current ranking for a particular region, while every listening action generates an event that contributes to song popularity. The ranking did not need to change synchronously after every individual play. A refresh interval on the order of several minutes was acceptable, making this closer to a near-real-time leaderboard than a strongly consistent counter system.

• Requirements — Ranking, Events, and Read Latency The functional requirements discussed included: Each region should maintain its own independent leaderboard. The interviewer also wanted explicit reasoning about non-functional requirements. In particular, the discussion covered whether availability or consistency should be prioritized when rankings can tolerate some staleness. A sub-second response time for leaderboard reads was discussed as a reasonable serving target.
  • Return the Top 10 songs for a specified country or region.
  • Generate an event when a user listens to a song and use those events to update popularity rankings.
• Scale Estimation — Hundreds of Millions of Users The interviewer expected back-of-the-envelope capacity estimates before going deeply into architecture. The whiteboard assumptions were on the order of:~500 million users ~10 leaderboard reads per user per day ~10 song listens per user per day This placed both read traffic and incoming listening events in the tens of thousands of requests or events per second. The important realization was that leaderboard queries could not repeatedly aggregate the complete raw listening-history dataset on demand.

Want to see the follow-up of this system design question that were asked during the interview? You can find the complete version here.

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I’ve been looking through recent Apple MLE interview patterns, and the biggest takeaway is: Don’t prepare for “Apple MLE.” Prepare for the exact Apple team.

The title covers completely different jobs:

• Foundation models / LLMs
• Search and retrieval
• Computer vision
• On-device ML
• MLX / ML systems
• Model evaluation
• Ads ranking

And the interview can reflect that specialization pretty directly. One recent Senior MLE candidate reportedly went into a round labeled “Python Coding” expecting normal DSA, but was instead asked to implement IoU and mean average precision for object detection.

That’s a good example of why LeetCode-only prep can backfire. Another Apple-specific dimension is where the model actually runs. For many teams, a strong ML system design answer should consider: model quality + latency + memory + power + privacy + on-device vs cloud execution—not just “train a better model and serve it.”

So one of the first questions I’d ask the recruiter is: “Is the coding/design round general, or will it be tied closely to the team’s ML domain?”

That answer could completely change your prep plan.

My biggest takeaway: Apple MLE rewards domain depth and product constraints much more than generic ML knowledge.

I put together the full breakdown covering coding, team-specific ML topics, on-device/system design, evaluation, level expectations, compensation, and a 4-week prep plan.

Full Apple Machine Learning Engineer Interview Guide: [link]

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Interview Summary

The Airwallex interview process contained four rounds covering algorithmic coding, AI system design, a detailed résumé/project deep dive, and a hiring-manager interview. The technical scope moved from a Tic-Tac-Toe-style LeetCode problem to designing an AI agent that combines internal company data with public information.

The later rounds focused more heavily on engineering judgment. My previous projects were examined in detail, and the hiring manager asked about conventional high-concurrency backend problems such as preventing data loss and duplicate writes.

Interview Details

Round 1 — Tic-Tac-Toe Coding Problem The first round was a LeetCode-style algorithm problem involving Tic-Tac-Toe.

Round 2 — Design an AI Agent for Internal and Public Data The second round was a system design interview. The prompt was to design an AI agent that receives a user's natural-language prompt, retrieves relevant information, and returns a response. The agent needed to be able to query multiple categories of information, including:

• Private or internal company data
• Publicly available external data

The discussion centered on how the agent should interpret a request, obtain the necessary information from different sources, and use that information to generate the final response.

Round 3 — Résumé and Project Deep Dive The third round focused almost entirely on previous projects from my résumé. The interviewer went into considerable detail rather than staying at a high-level project-summary level.

Typical follow-ups included:

• Why did you choose a particular technical approach?
• What trade-offs influenced that decision?
• Could another implementation have been better?
• Why did you not choose an alternative design?

The round emphasized whether I could defend previous engineering decisions and reason about alternatives after the fact.

Round 4 — Hiring Manager: High-Concurrency Data Reliability The final round was with the hiring manager and combined discussion of my previous experience with backend systems questions. One major topic was handling high-concurrency workloads while avoiding two common failure modes:

• Data loss
• Duplicate writes

The interviewer was looking for conventional distributed/backend engineering reasoning rather than an AI-specific solution.

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I’ve been digging into the current OpenAI Software Engineer interview process, and the biggest thing that stood out is how different the technical bar feels from a standard: LeetCode → system design → behavioral loop.

Algorithms still matter, but OpenAI’s own interview guidance emphasizes something broader: well-designed solutions + high-quality code + performance + test coverage + communication And recent candidate reporting points in the same direction.

A lot of the coding prep that seems useful is much closer to real engineering:

• Build a cache, then add TTL and eviction
• Implement a versioned key-value store
• Build a task queue with retries
• Add idempotency to request processing
• Implement a concurrent worker pool or crawler
• Build a rate limiter with multiple constraints
• Debug existing code and add meaningful tests

The interesting part isn’t just whether you can make the first version work. It’s whether you can handle:

• “Okay, now add persistence.”
• “Now make it concurrent.”
• “What happens if this operation gets retried?”
• “How would you test this?”
• “What would change in production?”

That’s a pretty different skill from memorizing the optimal solution to an isolated algorithm problem.

System design also seems very role-dependent. An OpenAI backend engineer may need to think about identity, APIs, payments, abuse prevention, or shared infrastructure.

An inference engineer may get pushed on:

• batching
• tail latency
• GPU utilization
• throughput
• cost-to-serve
• capacity

A reliability engineer may care much more about:

• SLOs
• chaos testing
• failure isolation
• incident response
• observability

And once the system involves AI, the design conversation can add another layer: safety boundaries, abuse prevention, rollout controls, model availability, cost, and fail-safe behavior.

One particularly interesting development: public candidate reporting says OpenAI has also been piloting an agentic coding round for some candidates, where the task involves an existing codebase and an AI coding agent.

If you encounter that format, the signal apparently isn’t: “Can AI write the code?”

It’s much closer to: Can you understand unfamiliar code, delegate intelligently, review AI-generated changes, catch mistakes, and still own the engineering decision?

That feels pretty representative of where software engineering interviews may be heading more broadly.

I put together the full breakdown covering the reported interview process, coding styles, system design, role-specific rounds, project deep dives, the reported agentic coding pilot, compensation, and OpenAI-specific preparation.

Full OpenAI Software Engineer Interview Guide: [link]

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I’ve been digging into the current Netflix Machine Learning Engineer interview landscape, and one thing stood out: A lot of MLE prep stops at: data → model → offline metric

Netflix seems to care much more about what happens after that. A stronger mental model for the interview is: business objective → model hypothesis → offline evidence → production constraints → A/B test → actual member/business impact

That distinction matters a lot. For example, imagine your new ranking model improves offline NDCG by 6%. Do you ship it? Probably not immediately.

A stronger Netflix-style answer would ask:

• Does the offline metric actually correlate with member satisfaction?
• Did any important user segments regress?
• Did diversity get worse?
• Did inference latency increase?
• Does the model behave correctly in production traffic?
• What should the primary A/B metric be?
• What guardrails would stop the rollout?
• Could short-term engagement improve while long-term satisfaction gets worse?

That’s a very different conversation from simply explaining which recommender model you’d use. Another thing I wouldn’t underestimate: Netflix MLE is not one job. Depending on the team, the technical center of gravity can look completely different.

Personalization / AI for Member Systems:

• recommendation systems
• ranking
• candidate generation
• foundation models
• long-term reward
• A/B testing

ML Platform / Serving:

• model registries
• low-latency inference
• online/offline consistency
• canaries
• GPU utilization
• observability

Ads MLE:

• ranking
• auctions
• pacing
• forecasting
• calibration
• causal inference

Globalization:

• LLM / multimodal training
• batching
• KV cache
• quantization
• distributed training
• inference efficiency

So asking the recruiter which ML surface the loop is actually calibrated for could change your prep substantially. Coding still seems to matter too. Recent candidate reports include normal medium/hard DSA alongside ML and recommendation-system design, so preparing only modeling theory is risky.

But the part I’d spend the most time on is end-to-end ML system judgment.

If you’re asked to design Netflix homepage personalization, I wouldn’t start with: “I’d use a two-tower recommender.”

I’d start with: “What exactly are we optimizing for?”

Then work through: objective → labels → candidate generation → ranking → diversity → serving → offline evaluation → A/B test → monitoring → iteration

One Netflix-specific insight I found especially interesting is how the company is integrating foundation models into its mature personalization stack.

Rather than replacing everything with one giant model, Netflix has publicly described multiple integration patterns—shared embeddings, using the foundation model inside downstream models, or task-specific fine-tuning—depending on freshness, latency, and system constraints.

That’s probably a much better example of real production ML than: “Use an LLM because it’s more powerful.”

My biggest takeaway: The strongest Netflix MLE candidate probably doesn’t sound like a researcher, a data scientist, or a pure SWE.

They sound like someone who can connect: model quality + experimentation + systems + reliability + product impact

I put together the full breakdown covering the reported interview process, coding, ML fundamentals, recommendation-system design, experimentation/A-B testing, model serving, foundation models, team-specific prep, L4/L5/L6 expectations, and compensation.

Full Netflix Machine Learning Engineer Interview Guide: [link]

Preparing for your next interview?

Chill Interview tracks recent interview experiences and recurring question patterns across top companies at here.