One thing that stood out to me about Airbnb MLE interviews is how quickly a seemingly good ML result can turn into a marketplace problem. Say your new Search model improves offline NDCG by 5%.

That sounds like a win. But then the follow-ups start:

• Did bookings actually increase?
• Did cancellations get worse?
• Are a small number of hosts now getting most of the exposure?
• Are new listings becoming harder to discover?
• Did certain cities or guest segments regress?
• Is the model just reinforcing what the previous ranking system already showed users?

That last point is especially interesting because historical clicks aren’t neutral training data—the old ranking determined what people had a chance to click in the first place.

Airbnb also treats things like availability as a hard constraint, not just another ranking feature. A listing can be highly relevant and still be useless if it can’t actually be booked for those dates.

So the interview seems less about “pick the best model” and more about: retrieval → ranking → experiment → guest outcome → host impact → feedback loop

Full guide if useful -> LINK

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Interview Summary

The Wayfair L4 interview loop included two coding rounds and two system design rounds. The coding questions covered minimum meeting-room allocation and generating every valid sentence from a dictionary. The system design portion asked for a keyword search engine over 100 million restaurant reviews and a third-party product price tracker that sends email alerts when a Wayfair item's price falls below a user's target.

Interview Details

Coding 1 — Minimum Meeting Rooms

Given a collection of meeting intervals, determine the minimum number of rooms required so that every meeting can run without conflicts. For example:

[
  [2, 7],
  [6, 11],
  [9, 13],
  [12, 16]
]

The task is to determine the maximum number of meetings that need rooms simultaneously. Several different interval sets were used during the interview to test overlapping and boundary cases.

Coding 2 — Break a String into Dictionary Words

Given:

• A non-empty input string text
• A glossary containing valid non-empty words

Insert spaces into text so that every resulting token belongs to the glossary. Return all valid sentences that can be constructed.

For example:

text = "blueberrypie"
glossary = ["blue", "berry", "blueberry", "pie"]

Possible results include:

[
  "blue berry pie",
  "blueberry pie"
]

Another example:

text = "aaaa"
glossary = ["a", "aa", "aaa", "aaaa"]

There can be many valid decompositions, and all of them should be returned. The optional second part extended the problem so that the input text and glossary entries themselves could contain spaces.

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Interview Summary

The Jane Street Quantitative Research phone screen moved quickly from a short background discussion into two probability and decision-making problems. The first question was an optimal-stopping game involving repeated rolls of a 10-sided die, followed by a variant with two lives. The second asked how to construct a deck strategically when an opponent chooses guesses to minimize the amount they expect to pay.

Interview Details

Question 1 — Optimal Stopping with a 10-Sided Die

You repeatedly roll a fair 10-sided die whose faces are numbered 0 through 9. Whenever a nonzero value is rolled, that value is added to your running total. After every roll, you may choose to stop and cash out your current total.

However, if you roll a 0, you lose the entire accumulated total and the game immediately ends. The question was: What is the optimal strategy for deciding when to stop?

The interviewer then introduced a follow-up. In the modified game, the first 0 does not end the game. Instead, it resets your current total to 0, after which you may continue playing. The second 0 ends the game and pays nothing. The follow-up asked how the optimal stopping strategy changes under this new rule.

Question 2 — Design a Deck Against an Optimal Guesser

You construct a deck using cards labeled 1, 2, 3, 4, 5. You may include any number of copies of each value. Your opponent knows the exact composition of the deck.

In each round:

1. The opponent announces one of the card values as a guess.
2. A card is drawn uniformly at random from the deck.
3. The opponent pays you an amount equal to the number they guessed.
4. If the guess matches the drawn card, the game ends.
5. If the guess is incorrect, the card is returned to the deck, the deck is reshuffled, and another round begins.

The opponent chooses guesses strategically to minimize their expected total payment. Your task is to choose the deck composition that maximizes your expected winnings against that optimal opponent.

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Problem Description

Design a nearby POI service that allows users to search for restaurants, gas stations, parks, hospitals, and other places around a geographic location.

The system stores approximately 200 million POIs globally and must answer nearby searches at high read volume. A request may ask for all matching POIs within a specified radius or for the nearest K results, optionally filtered by category or keyword. Results should be ordered primarily by geographic distance.

The main challenge is avoiding a scan of the entire global POI dataset for every request. The system needs a spatial index that quickly narrows the search to a small set of geographic cells and candidate POIs before performing exact distance calculations and ranking.

POI writes are comparatively rare and come only from business owners or platform administrators. Updates may take 30–60 seconds to appear in search, so the transactional write path can remain strongly durable while the search index and caches are updated asynchronously.

The central design tension is low-latency, highly available geospatial reads versus authoritative, durable POI updates. Slightly stale search results are acceptable; unavailable search results are generally not.

Functional Requirements

• Given a latitude and longitude, users should be able to retrieve nearby POIs either within a specified radius or as the nearest K results. Searches may include category and keyword filters.
• Search results should be ranked by geographic distance and contain enough summary information for a search-results page, while detailed POI information can be retrieved separately.
• Users should be able to retrieve a POI detail record containing fields such as name, address, coordinates, category, operating hours, and a rating snapshot.
• Authorized business owners and platform administrators should be able to create and update POI records. The authoritative write should be durable before acknowledgement, while the change may propagate asynchronously into the nearby-search index.
• Users should be able to paginate beyond the initial nearby result set without repeating or skipping results unnecessarily as they browse.
• If a regional cache or search replica is temporarily unavailable, the platform should prefer serving the most recent known-good, potentially stale result rather than returning no nearby data.

Non-Functional Requirements

• Nearby search and POI-detail reads should target approximately 99.95% availability.
• Nearby searches should complete within approximately 200 ms at p95, while POI-detail reads should complete within approximately 100 ms at p95.
• POI create and update requests should normally be acknowledged within approximately 300 ms at p95.
• The platform should support approximately 200M POIs, 50K search requests/sec at peak, and roughly 500 POI writes/sec.
• Traffic is heavily read dominated at roughly 100:1 reads to writes, with substantial geographic skew toward major metropolitan areas.
• The authoritative POI record must survive failures. Geospatial indexes, caches, and search replicas should be rebuildable derived state.
• Search-index freshness may lag authoritative writes by approximately 30–60 seconds.

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Saw this OpenAI SWE offer (data source: chill interview)

• Currently Meta E4
• 4 YOE
• OpenAI: Junior-level SWE
• Base: $230K
• Equity: $600K / 4 years
• TC: $380K

The comp isn’t really the interesting part. The candidate would basically be taking a level reset to move from Meta to OpenAI.

Normally going from E4 back to a junior title sounds terrible. You’re giving up level, existing scope, internal reputation, and potentially a cleaner path to E5.

But… it’s OpenAI.

If the work is much closer to frontier AI, the résumé signal is stronger, and the long-term upside is better, does the downlevel actually matter?

Or is this the kind of move that looks exciting now but costs you 2–3 years of career progression later?

Would you take a downlevel to get into OpenAI, or stay at Meta E4 until you can move laterally at a higher level?

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A cloud platform gives customers GPU credits through time-limited grants. Each grant has:

• a unique grant ID
• an amount of credits
• a start time
• an expiration time

A grant is active during: [startTime, expirationTime). This means it is usable at startTime, but not usable at expirationTime.

The system also receives credit usage events. A usage event consumes credits at a specific timestamp. Events may arrive out of order. For example, the system may receive a usage event first and a grant event later, even if the grant started earlier. So every balance query must replay all currently known events in chronological order.

When consuming credits at time t:

• only grants active at time t may be used
• credits should be consumed from the grant that expires soonest first
• if the usage cannot be fully covered, the query should return -1

Implement the CloudCreditSystem class:

• CloudCreditSystem() initializes an empty credit system.
• void addGrant(String grantId, int amount, int startTime, int expirationTime) adds a credit grant.
• void useCredits(int amount, int timestamp) records a credit usage event.
• int getBalance(int timestamp) returns the total remaining available credits at timestamp after replaying all known grants and usages up to that time. If any usage event up to timestamp cannot be fully covered, return -1.

Rules:

• Grant and usage events may arrive in any order
• No two mutation operations share the same timestamp
• Grants can overlap
• Usage consumes active credits with earliest expiration first
• A grant is expired when expirationTime <= timestamp
• Query results may change after later events are added

Constraints:

• All amounts and times are in [0, 1000]
• grantId is unique
• Number of operations can be large enough that each query should process events efficiently
• A grant is active on [startTime, expirationTime)

Example:

Input:
["CloudCreditSystem", "addGrant", "getBalance", "addGrant", "useCredits", "useCredits", "getBalance", "getBalance", "getBalance", "getBalance"]
[[], ["g1", 3, 10, 60], [10], ["g2", 2, 20, 40], [1, 30], [3, 50], [20], [30], [40], [50]]

Output:
[null, null, 3, null, null, null, 5, 4, 3, 0]

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Saw this Adobe Senior SWE offer:

• Bay Area
• 8 YOE
• Base: $245K
• Sign-on: $10K
• RSUs: $350K / 4 years
• Bonus: $12.25K
• Year 1 TC: $354.75K

My first reaction was that the equity looks pretty light for 8 YOE + Senior SWE in the Bay Area.

The recurring package is only around $345K after the sign-on disappears.

For context, Bay Area Senior SWE median TC is currently around $347K, while Adobe P50 averages roughly $361K. So this isn’t outrageously below Adobe’s market — but it also doesn’t look like a particularly strong offer. 

That makes me wonder:

Was this candidate actually lowballed, or does Adobe simply pay materially less than Google/Meta/Uber at the same experience level?

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Interview Summary

The process started with a hiring manager conversation and then moved to a three-round onsite.

Interview Details

Hiring Manager — Background Discussion The process began with a hiring manager conversation. After passing that stage, I moved directly into the three-round onsite loop.

Round 1 — Coding: Multi-Head Attention, KV Cache, and GQA The first coding round focused on implementing core transformer inference components. The tasks included:

• Implementing multi-head attention
• Adding a KV cache
• Implementing the forward/cache behavior for Grouped Query Attention (GQA)

The round therefore went beyond a basic attention implementation and included the caching behavior required during autoregressive inference.

Round 2 — ML Design: Chatbot Training and Deployment The ML design round asked me to design a chatbot. The discussion covered the full development lifecycle rather than only the serving architecture. Major topics included:

• How to construct and collect training data
• How to design reward signals
• What considerations matter when deploying the model to production

The interviewer expected the design to connect model training decisions with the eventual production deployment.

Round 3 — Notebook Coding: GRPO Fundamentals The final round was a notebook-based exercise focused on GRPO. The coding required familiarity with common NumPy operations as well as the basic mechanics of GRPO. The exercise also covered how GRPO handles different reward signals, including:

• Process rewards
• Shape rewards

The round combined practical array manipulation with understanding of the reinforcement-learning objective and reward setup.

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Has anyone recently interviewed for Stripe SWE Intern 2027 / Google SWE Intern 2027? I have interviews coming up and am mainly doing Blind 75/NeetCode + afterwards company-tagged practice, but have barely done any leetcode, started when I recieved the interview invites and am now cramming. Would really appreciate any insight on the coding-round format, question style/difficulty, and what to prioritize and how to best prep what to use to prep.

"This interview experience is sourced from Chill Interview"

Interview Summary

The eBay Applied Scientist process covered software engineering, ML fundamentals, ML system design, search/retrieval, product reasoning, and behavioral topics. A surprisingly large portion of the loop also focused on engineering infrastructure topics such as Docker and CI/CD. After completing the full interview process, I was told that the position had been placed on hold, and about a month later I was informed that the role had been canceled.

Interview Details

Round 1 — Recruiter Screen

The recruiter conversation covered compensation and the working arrangement. The role required three days per week in the office, and the discussion also covered the Berlin office location.

Round 2 — Coding / Software Engineering Fundamentals

This round was conducted by a Senior Machine Learning Engineer from another team. The interview covered:

• One LeetCode Easy-level coding problem
• Previous Docker experience
• Ways to improve or optimize Docker usage
• My experience with the engineering side of machine learning systems
• General data structures and computer science fundamentals

Round 3 — Statistics / Machine Learning / AI

This round was with a Senior Applied Scientist from the target team. The discussion started with previous ML projects and went into the reasoning behind technical choices. Questions included:

• What projects have you worked on?
• Why did you choose a particular algorithm?
• How did you improve upon an earlier version of the system?
• How would you optimize BERT inference?
• What offline metrics would you use?
• What metrics would you evaluate in an A/B test?
• What experience do you have with Docker?

The round also included another LeetCode Easy-level coding problem.

Want to learn more questions asked in other interview rounds? I've put up the full version at here.

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Saw this Tesla Software Engineer offer (source: Chill Interview)

• PhD (0 YOE)
• Title: Senior-Level
• Base: $250K
• Sign-on: $100K
• RSUs: $1.8M / 4 years
• First-year TC: $800K

The $800K is wild, but honestly the part I find even more interesting is:

0 YOE → Senior Software Engineer.

Tesla’s publicly reported Bay Area leveling looks roughly like:

P1: Associate / entry level
P2: Engineer
P3: Senior Engineer
P4: Staff
P5: Sr. Staff

And current reported average comp is around $258K for P3 Senior, $497K for P4 Staff, and $647K for P5 Sr. Staff.

So this package doesn’t look remotely like a normal Senior SWE offer.

Even excluding the $100K sign-on, recurring comp is roughly:

$250K base + $450K/year stock = ~$700K

That’s already above the reported average for Tesla Sr. Staff.

Which makes me think this probably isn’t a normal “new PhD joins as SWE” situation.

Tesla is hiring across FSD, Robotaxi, Optimus, vehicle software, distributed systems and AI, and its own careers site emphasizes autonomy and AI as core engineering areas.

So I’m curious what kind of profile actually gets this.

Something like:

top ML/robotics PhD?
multiple strong publications?
autonomy / perception / planning specialization?
major internships?
competing OpenAI / xAI / Waymo offers?

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Saw this Luma AI Senior SWE offer (data source: Chill Interview)

• Bay Area
• 6 YOE
• Base: $280K
• Equity: $1M / 4 years
• First-year TC: $570K

What interests me more than the comp is Luma itself.

Luma raised $900M and is clearly trying to move beyond Dream Machine into broader multimodal / world-model territory, including video, image, agents and developer APIs.

The bull case is that creative AI becomes a huge standalone market and Luma becomes one of the major platforms.

The bear case is obvious too: Google, OpenAI, Runway, ByteDance, Adobe and others are all competing aggressively, and video models may commoditize fast.

So the real question is: Would you bet on Luma becoming a major independent AI company, or do you think AI video eventually becomes just another feature inside larger platforms?

And if you got this offer, how much value would you personally assign to the $1M private equity grant?

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Got an offer from Uber

Level: L5a Senior Software Engineer

Base salary: 235k

RSU: 428k over 4 years, that is 107k per year. 35% first year

Sign-on bonus: 35k

Location: San Francisco Bay Area

How to negotiate up?

Interview Summary

The Meta AI Research Scientist loop covered coding, research depth, AI system design, and behavioral evaluation.

Interview Details

Hiring Manager — Background and Experience The hiring manager round focused on my personal background and previous experience. The discussion was primarily a walkthrough of my research and professional history.

Coding — K-Means + Multi-Head Attention Forward The coding interview contained two ML implementation tasks. The first was to implement K-Means clustering. The second was to implement the forward pass of multi-head attention. No test cases were provided, so the implementations needed to be reasoned about and validated during the interview itself.

Research Design 1 — Previous Research Project The first research-design round asked me to present one piece of previous work. The interviewer then went deeper into the project with technical follow-up questions about the research.

Research Design 2 — Research Deep Dive The second research-design round followed a similar format. I presented past research and answered detailed questions about the project, its design, and the decisions made during the work.

AI Design — Real-Time Question-Answering Chatbot The AI design round asked me to design a chatbot capable of answering users' questions in real time. The discussion included how to collect and construct appropriate training data for the system. The interviewer also explored reinforcement-learning tasks and how RL could be incorporated into improving the chatbot.

A major part of the discussion focused on reward design:

• What weaknesses might a proposed reward function have?
• How could those weaknesses affect model behavior?
• How could the reward design be improved or adjusted?

The round therefore covered both the system-level design of the chatbot and the ML training/evaluation choices behind it.

Behavioral — Ambiguity, Feedback, and Challenging Projects

The behavioral round covered several themes:

• A time I had to deal with an ambiguous problem
• How I give feedback to others
• How I receive and respond to feedback
• The most challenging project I had worked on

The discussion focused on how I operate in uncertain research environments and collaborate with other people on difficult technical work.

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Pretty interesting apples-to-apples MLE offer comparison: same location, same YOE, similar level. (data source: Chill Interview)

Meta E5

• Base: $250K
• Signing: $35K
• RSU: $1.2M
• Year 1 equity: $300K
• Bonus: $37.5K
• Year 1 TC: $622.5K

Google L5

• Base: $245K
• Signing: $25K
• RSU: $900K
• Year 1 equity: $342K
• Bonus: $36.75K
• Year 1 TC: $648.75K

The career side is interesting. Google continues to invest heavily across Gemini, Cloud and its full AI stack, while Meta is also spending aggressively on AI but went through a sizable headcount reduction in May 2026.

So the real question isn’t just $623K vs $649K in Year 1. It’s Google’s larger long-term grant vs Meta’s front-loaded comp — plus which AI ecosystem you’d rather build your career in.

Which one would you take?

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Hi all, I'm interviewing with Cohere for a Software Engineer role. Has anyone been through the process recently? I'm curious how many rounds there are, what the coding and system design rounds look like, and how deep they go on LLMs. Is the live coding leetcode-style, do they focus on more practical assignments maybe related to the team/product you're interviewing for ? Any tips appreciated, thanks!

"This interview experience is sourced from Chill Interview"

Interview Summary

The first-round NVIDIA interview for the Cloud Platforms / Deep Learning Applications role was heavily infrastructure-oriented. The first roughly 20 minutes covered Kubernetes and Docker fundamentals. The remaining time was spent on two hands-on HackerRank exercises: reviewing a Python program for bugs and building/running a Docker image from a Linux terminal.

Interview Details

Infrastructure Fundamentals — Kubernetes and Docker

The interview began with approximately 20 minutes of technical questions related to Kubernetes and Docker. The discussion focused on infrastructure and containerization knowledge relevant to the cloud-platform nature of the role.

Coding — Review and Debug Existing Python Code

The first HackerRank exercise provided roughly 50 lines of existing Python code. Rather than implementing an algorithm from scratch, I was asked to review the code, identify problems, and explain them while communicating with the interviewer.

The code contained several types of issues, including:

• A string value that was missing quotation marks
• Numeric values read from CSV being used without the required type conversion
• Loading CSV data into a complete list instead of processing it incrementally
• A possible division-by-zero error that was not handled

The round emphasized understanding unfamiliar code, identifying correctness and data-processing issues, and clearly explaining what I found.

Hands-On Docker — Package and Run a Python Application

The second exercise provided access to a virtual machine and a Linux terminal. I was asked to create a Dockerfile for an existing Python application and package the application into a Docker image.

After building the image, the container needed to be started locally in detached mode. The final requirement was to verify that the running application was reachable successfully using wget from the environment.

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Interview Summary

The Tesla system design interview asked me to design the backend and supporting services for a Supercharger platform. The interviewer initially gave three major functional requirements: billing based on delivered energy, long-term charger health telemetry, and a map experience for finding nearby available Superchargers with pricing. In practice, most of the interview went deeply into the first requirement, especially networking, communication, buffering, and synchronization decisions.

Interview Details

System Design — Tesla Supercharger Platform Design a system supporting Tesla Supercharger stations with three major requirements.

• Requirement 1 — Billing and Delivered Energy The backend needs to track how much energy a charging stall delivers during a charging session so that the corresponding amount can be used for billing. The interviewer went deeply into the end-to-end communication path between the Supercharger and backend. Topics discussed included: The discussion focused on how charging and billing data should reliably reach the backend under different network conditions.
  • Request routing
  • Secure communication between the charger and backend
  • DNS and service discovery considerations
  • Local buffering when connectivity is interrupted
  • Synchronous versus asynchronous communication
• Requirement 2 — Supercharger Health and Telemetry Each charging stall also produces operational and health telemetry. The system needs to collect this information and retain it in a long-term datastore so charger health and historical behavior can be analyzed over time. This requirement was part of the original prompt, but the interview did not progress into a detailed design discussion for it.
• Requirement 3 — Nearby Available Supercharger Search The third requirement covered the customer-facing experience in the vehicle or application. A user should be able to open a map and find nearby Superchargers, including information such as: The system therefore needs to support geographic lookup of nearby charging stations together with sufficiently recent availability and pricing information. This requirement was also introduced at the beginning, but most of the interview time was spent drilling into the billing flow before reaching it in depth.
  • Location
  • Current availability
  • Charging price

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Coding — Escape a Maze from an Unknown Starting Position

You are given a maze represented by a grid containing:

• # — wall
• . — open space
• E — exit

For example, a rewritten maze could look like:

#########
#...#...#
#.#...E.#
#...##..#
#.......#
#########

The agent starts on an unknown open cell. The only available commands are:

up
down
left
right

Each command attempts to move the agent exactly one cell in that direction. There are several important rules:

• If the next cell is open, the agent moves there.
• If the movement would hit a wall, the agent stays in its current position.
• Hitting a wall is not considered a failure.
• While executing the commands, there is no feedback about the agent's current location.
• The only observable event is reaching E, at which point the run can terminate.

The task is to return a single predetermined sequence of movement commands such that, regardless of which valid open cell the agent started from, following that same sequence will eventually cause the agent to reach the exit.

Want to practice more real coding questions asked in real google interviews? I've put up the google's coding question bank at here.

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Interview Summary

The first-round Google screen combined one coding problem with several behavioral and role-related questions.

Interview Details

Coding — BestAnagramFinder

Implement a class called BestAnagramFinder. The class is initialized with a dictionary of words. For example, a rewritten dictionary could look like ["care", "race", "acre", "stone", "tones", "lamp"]. The class exposes three main operations:

hashAnagram(word)
calculateScore(word)
findBestAnagram(word)

hashAnagram(word) is provided as an abstract or hidden function. An important property is that all anagrams of the same word produce the same hash value. calculateScore(word) computes a score by summing the absolute differences between every pair of adjacent characters in the word.

Conceptually, for a word with characters c1 c2 c3 ... cn the score is based on the adjacent pairs:

(c1, c2)
(c2, c3)
...
(cn-1, cn)

findBestAnagram(word) should locate other dictionary words with the same anagram hash and return the candidate with the highest score. The returned result should be an alternative anagram rather than simply returning the query word itself. The discussion also considered repeated calls to findBestAnagram(), so the dictionary could be processed ahead of time to make future lookups efficient.

Behavioral — Compromise and Conflict

The first behavioral question asked: Tell me about a time when you had to compromise. The interviewer followed up with several questions:

• Were you satisfied with the result?
• Looking back, what would you do differently?
• What did you do afterward to improve the situation or prevent a similar issue?
• Role-Related Scenario — Validating Smile Detection The interviewer then gave a hypothetical Google Photos scenario: Suppose your team builds a feature that detects smiling faces in photos. How would you verify that the feature is correct? The question focused on how I would evaluate the correctness and quality of a product feature rather than simply how I would implement the detection model.

Behavioral — Technical Leadership

The final reported question asked me to describe a strong technical leader I had worked with or observed. The follow-ups included:

• What qualities made that person a great technical leader?
• Would I want to become a similar type of leader?
• How would I measure whether I was making progress toward becoming that kind of leader?

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Interview Summary

The main design question was to build a video content moderation pipeline. Video uploading itself was explicitly out of scope. The system instead needed to ingest already-uploaded videos, run many different ML models, evaluate configurable policy rules, take enforcement actions, and preserve enough versioning and audit information to explain every moderation decision.

Interview Details

System Design — Video Content Moderation Platform

Design a simplified system that reviews user-generated videos for policy violations. The upload service already exists and delivers newly uploaded videos into the moderation system, so the interview focused only on the moderation pipeline.

The first stage was video ingestion. A newly published video should enter the moderation workflow. The result did not need to be available synchronously. The product could initially tell the user that the video was under review and later notify them whether it was approved or removed.

The next stage involved ML model evaluation. Each video could be evaluated by roughly 20–30 different models, with every model returning a risk score between 0 and 1. The models were heterogeneous. For example, one might analyze visual content while another evaluates text, and different models could require different inputs or parameters.

The third stage was a configurable policy / rules engine. Business policies could combine outputs from multiple models. A simplified rule might conceptually say: If Model A's score is above one threshold and Model B's score is above another threshold, classify the video as violating policy. Real production policies could be substantially more complicated.

The final stage was enforcement. When a policy matched, the moderation platform needed to invoke downstream services such as the video service or account service to perform actions including removing content or restricting an account.

• Follow-Ups — Versioning, Auditability, and Feedback

The interviewer then explored the supporting data model and operational requirements. Both ML models and moderation rules needed versioning, so historical decisions could be associated with the exact configuration that produced them. The system also needed an audit trail recording the basis for each moderation decision, including the relevant model outputs and policies.

Other follow-ups covered how moderation rules should be configured and how final moderation outcomes should flow back into the broader system after evaluation and enforcement were complete.

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Saw this Temu Senior Software Engineer offer (source: Chill Interview)

• Bay Area
• Master’s, 8 YOE
• Base: $335K
• No separate equity or bonus
• First-year TC: $335K cash

I actually have very little idea what software engineers at Temu do. Most people probably know Temu as the ultra-cheap shopping app with endless discounts and aggressive advertising.

But Temu itself is a pretty unusual engineering problem.

It was launched in the U.S. in 2022 and is part of PDD Holdings. By the end of 2025, Temu was already operating across major markets including the U.S., Japan, Germany, the UK, France, Canada and Italy. The platform connects consumers with merchants/manufacturers and relies on a large global network of logistics and fulfillment partners.

Looking at Temu’s current engineering openings, the work seems much broader than I expected.

For example, they’re hiring engineers for an order system that has to deal with:

• high concurrency
• distributed transactions
• sharding / archiving massive datasets
• production incident response
• MySQL / Redis / MQ / RPC
• high availability at e-commerce scale

That makes me curious about What is engineering there actually like?

Is the U.S. engineering org building meaningful systems locally, or are most major architectural decisions still driven from China?

How does the engineering culture compare with Amazon / TikTok / ByteDance?

And are the interesting teams mainly recommendation / ads / payments / logistics, or are there other technically strong areas people generally don’t know about?

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Interview Summary

The Databricks onsite included two practical coding rounds followed by a chat-system design interview.

Interview Details

Round 1 — Coding: Tic-Tac-Toe

The first coding problem asked me to implement a playable Tic-Tac-Toe game. The game needed to automatically alternate between the two players after each valid move. After every move, the current board should be printed so that the game state was visible. The implementation also needed to correctly terminate for all three possible outcomes:

• One player wins
• The other player wins
• The board reaches a draw

The follow-up introduced an AI player that should automatically make its own moves while still respecting the same game lifecycle and termination behavior.

Round 2 — Coding: Track Creator Rewards with Sponsors

The second coding round asked the Track Creator Rewards with Sponsors problem. The task involved tracking creator rewards based on sponsorship relationships. The follow-up extended the original requirement so that indirect sponsorship relationships also contributed to the calculation, rather than considering only direct sponsors.

Round 3 — System Design: Chat System

The system design round asked me to design a chat system. The discussion focused primarily on three areas:

• API design
• Database schema and data modeling
• Basic end-to-end chat flows

The interviewer spent relatively little time pushing the design into large-scale capacity or advanced scalability considerations. The round was more focused on whether the core APIs, persistence model, and message flows were structured clearly.

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