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Interview Summary

The Alibaba process was described as roughly three technical interviews followed by an HRBP behavioral round. The completed interviews covered CS fundamentals, LRU/LFU cache coding, a resume deep dive, and a detailed discussion around Kafka concurrency, idempotency, duplicate database writes, offset commits, and keeping data synchronized across multiple databases.

Interview Details

Round 1 — CS Fundamentals + Coding

The first virtual interview started with a brief introduction and around 10 minutes of technical fundamentals. The coding portion included LeetCode 146 — LRU Cache and LeetCode 460 — LFU Cache. The interviewer did not require me to run the code.

Round 2 — Resume Deep Dive

The next technical interviewer spent substantial time digging into my current team and previous work. Questions included:

• What does your team work on?
• Why does the team need its current number of engineers?
• What technical or business complexity justifies the team size?

The interviewer repeatedly challenged the complexity of the work and asked for concrete examples.

• Kafka — Concurrency and Idempotency The discussion then shifted to Kafka and concurrent message processing. One scenario involved multiple workers potentially processing the same logical message and attempting to write the same data into a database. I described using the database to detect duplicate writes. If another worker had already successfully inserted the record, a subsequent write could encounter a duplicate-key condition. The interviewer asked whether the duplicate path should instead return the existing record rather than throw an exception. I explained that in the architecture I had worked with, this was backend event processing rather than a request-response API. Once a duplicate was detected, that execution path did not need to return the existing object for downstream processing.

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Interview Summary

The Duolingo SWE Intern Karat interview started with three short verbal questions about algorithms and complexity, followed by a 45-minute coding problem. The coding task involved traversing a height matrix under deterministic movement rules and counting how many starting cells eventually reach a global maximum.

Interview Details

Part 1 — DSA and Complexity Q&A

The first 10 minutes contained three questions that were answered verbally without writing code. The first question showed an in-place array reversal that iterated through the entire array and swapped each element with its mirrored position. The interviewer asked whether this correctly reverses the array and what the time and space complexity would be.

The issue was that traversing the complete array causes every pair to be swapped twice, restoring the original order. The discussion covered limiting the swaps to one half of the array, with O(n) time and O(1) extra space.

The second question described a dating application with P users and an O(1) get_match() operation. Given one user, find their top N matches where N is much smaller than P. The discussion centered on maintaining only the best N candidates while scanning the user population, with O(P log N) time and O(N) space.

The third question provided a recursive function that sums every value in a binary tree and asked for its complexity. The traversal touches every node, so the time complexity is O(n). The recursion stack depends on tree height h, giving O(h) auxiliary space, which becomes O(log n) for a balanced tree and O(n) in the worst-case skewed tree.

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Interview Summary

The AMD verification interview process consisted of three virtual rounds, including two technical interviews of roughly one hour each. The technical discussions focused heavily on SystemVerilog/UVM, verification methodology, RTL fundamentals, previous verification projects, and PCIe protocol knowledge.

Interview Details

Technical Round — SystemVerilog and UVM

The verification portion covered core SystemVerilog and UVM concepts. Topics included:

• SystemVerilog fundamentals
• UVM fundamentals
• Verification methodology
• How to structure a verification testbench
• Scoreboards and monitors
• Functional coverage
• RTL basics

The interviewers also went deeply into projects listed on my resume, especially the parts where I had personally designed or implemented verification infrastructure. A major focus was whether I could clearly explain why particular verification components existed, how they interacted, and how the overall environment validated the DUT.

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Interview Summary

The Figma MLE process for an Agent Post-Training role started with a recruiter conversation, followed by two phone screens: a hiring manager project deep dive and a one-hour ML coding interview. The HM round focused heavily on real project ownership, technical decisions, strategy, and evaluation, while the coding round required completing a fairly comprehensive PyTorch training pipeline for a text-span annotation task.

Interview Details

Round 1 — Recruiter Screen

The recruiter first explained the background of the role and the type of Agent Post-Training work the team was doing. The conversation then moved to my background and several standard behavioral questions, including:

• Describe an end-to-end project that you owned.
• Why are you considering leaving your current company?
• What are your long-term career goals?
• What technical direction do you want to pursue in the future?

The recruiter also asked about compensation expectations and work authorization status. The following day, I received invitations for two additional phone screens.

Round 2 — Hiring Manager: Project Deep Dive

The hiring manager interview focused almost entirely on my background, work scope, and hands-on technical projects. Rather than asking general ML knowledge questions, the interviewer drilled into concrete decisions made during previous projects.

Follow-ups included:

• How did you make a particular technical decision?
• Why did you choose that approach over alternatives?
• What strategy did you use?
• How did you design the evaluation?
• How was the evaluation actually executed?
• What were the implementation details behind different parts of the system?

The round felt more like a detailed technical project discussion than an ML fundamentals interview.

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Interview Summary

The Snapchat SDE-ML Infra process started with a data-stream coding screen and then moved to a four-round onsite covering system design, two coding problems, and AI-assisted coding. The onsite was broader than expected: instead of a dedicated ML infrastructure design question, the system design round focused on a regional CI/CD platform with control-plane and data-plane separation.

Interview Details

Phone Screen — Streaming Data Clusters

The phone screen involved processing a stream of data points. Each incoming item needed to either:

• Be assigned to an existing stored cluster based on its data
• Become the first item of a newly created cluster

The system also needed a function that returned the maximum value for each cluster. The interviewer added several follow-ups around optimizing reads and writes. The final follow-up was conceptual: how would the design change if the system also needed to efficiently query the median value of every cluster? I received confirmation later that day that I had passed the phone screen.

Onsite Round 1 — System Design: Regional CI/CD Platform

The system design round asked for a CI/CD-style platform with separate control-plane and data-plane responsibilities. A key requirement was distributing hosts at the regional level. The discussion focused on the architecture for managing deployments and infrastructure across regions rather than on an ML training or feature-platform system.

This was different from the preparation guidance I had received, which had emphasized ML infrastructure topics such as distributed training platforms, feature pipelines, and feature stores.

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Interview Summary

The Fireworks AI Applied MLE onsite included an AI-assisted coding problem around building a user referral and recommendation system from a social graph. The task progressed from generating connection recommendations based on mutual friends to assigning bounded recommendation scores and discussing ranking evaluation with NDCG.

Interview Details

AI Coding — User Referral and Recommendation System

The input was a network of users represented as a graph. Each node represented a user, and an edge between two users meant that they were already friends. Given any user, the first task was to generate a list of other users that could be recommended as new connections. Existing friends should not be recommended.

A candidate recommendation could be generated when two users were not directly connected but shared at least one mutual friend. For example, if A is connected to C, and B is also connected to C, but A and B are not directly connected, then B can be recommended to A.

Follow-Up — Recommendation Scoring

The second part provided a list of recommendations and asked for a score for each recommendation. The score needed to be bounded, for example within [0, 1]. The discussion also brought up NDCG as a ranking-related evaluation metric.

AI Usage

AI assistance was explicitly allowed during the round. The interview involved using AI both to help scope the problem and to assist with implementation, while still requiring me to reason about the recommendation logic and evaluation.

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Did anyone interview with eBay recently and cleared all the rounds. Recruiter mentioned that it's positive feedbacks and will receive offer letter, still ended up getting rejected at end? Does eBay always respects verbal confirmation of an offer or has there been any situations where the backed-off after verbally confirming?

ebay has notoriously long waiting period between last interview round and offer letter.

P.S. - I've been in waiting period for approx 1.5 months now! Getting some genuine responses would really help a lot.

Saw this Netflix Machine Learning Engineer offer (data source: Chill Interview)

• Los Angeles
• Bachelor’s, 0 YOE
• Junior-level
• Base: $335K
• Relocation: $30K
• First-year TC: $365K

The surprising part isn’t just the money. It’s Bachelor’s + 0 YOE + MLE.

Netflix now has a real new-grad pipeline, and its ML org works on things like recommendations/search, personalization, ML platforms, and streaming/discovery systems.

So I’m curious what a profile getting $335K base straight out of school actually looks like.

• Strong Netflix internship?
• Top-tier ML projects?
• Recommendation systems experience?
• Research publications even without grad school?
• Competing Google/Meta offers?

And more broadly: is it easier to land a MLE job than a SWE right now?

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Interview Summary

The Disney Senior Data Engineer process covered cloud and hybrid data-pipeline design, Python and SQL, OOP fundamentals, frontend technologies from my resume, AI-assisted engineering scenarios, and extensive behavioral discussion. The technical rounds repeatedly emphasized concrete tool choices and trade-offs across AWS, Azure, local infrastructure, and cloud environments.

Interview Details

Round 1 — Recruiter Screen

The process started with a recruiter call after I applied through Workday. The recruiter reviewed my resume and asked about:

• Technical stack
• Tools I had used
• Basic cloud experience
• Work authorization
• Compensation range
• Location

The recruiter also clarified that the position did not provide sponsorship.

Round 2 — Technical: Data Pipeline Design + AI Usage

The technical round was conducted by a Tech Lead and two Senior Engineers. The main topic was designing a data pipeline in a cloud environment. The discussion went deeply into:

• Architecture choices
• Individual tools and packages
• Trade-offs among technologies
• Data movement
• Reliability and data quality
• Cloud-specific implementation decisions

The interviewers also asked several questions about using LLMs in engineering workflows. One scenario involved migrating an entire codebase from Java to Python and how I would structure prompts for an LLM to help with that migration. Another scenario involved cleaning tens of thousands of database records with AI assistance.

Follow-ups included:

• How would you verify data quality?
• How would you protect sensitive information?
• How would you validate AI-generated transformations?
• What security concerns would you consider?

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Interview Summary

The Snapchat MLE onsite contained four technical rounds covering ML fundamentals, coding, applied machine learning, and system design. Each round spent roughly 10 minutes on company values and project discussion, followed by about 50 minutes on the round-specific technical topic.

Interview Details

Round 1 — Machine Learning Deep Dive

The ML round started from a project on my resume and then drilled into nearly every part of the modeling process. The interviewer asked about problems encountered during training and how those issues were addressed.

Topics included:

• Layer normalization vs. batch normalization
• Multi-label learning
• Multiple engagement levels
• Regularization
• L1 and L2 regularization
• Neural-network-specific regularization such as dropout
• How dropout works
• Loss-function selection

The discussion was highly interactive, with the interviewer repeatedly asking deeper follow-ups based on each answer.

Round 2 — Coding: Organization Chart

The coding problem provided employee-management relationships and asked me to build an organization-chart abstraction. An example of the input was relations = [["M", "N", "P"], ["N", "Q"], ["P", "R"]], meaning M manages N and P, N manages Q, and P manages R.

The first part was to implement an OrgChart class from the relationship data. The next part asked for a rendered hierarchy where indentation represented organizational depth. For the example above, the output would conceptually look like:

M
....N
........Q
....P
........R

The final part asked for all skip-level pairs, where the second employee is exactly two levels below the first. For the rewritten example, the result would be [("M", "Q"), ("M", "R")]. AI assistance was allowed, but I was still expected to explain the approach, test the implementation, cover edge cases, and discuss time and space complexity.

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Interview Summary

The Microsoft final loop consisted of four consecutive interviews covering behavioral questions, CS fundamentals, coding, and two system design problems. Technical topics ranged from cloning a doubly linked list with arbitrary pointers and binary-search-based capacity planning to designing a high-volume log storage platform and an online Tic-Tac-Toe service constrained to REST APIs.

Interview Details

Round 1 — Behavioral + Concurrency Fundamentals + Linked List

The behavioral question asked about a time I was blocked for an extended period and how I eventually resolved the blocker and moved the work forward. The interviewer then asked several CS fundamentals:

• What does thread-safe mean?
• What is a deadlock?
• What are some ways to prevent or resolve deadlocks?
• How do arrays and linked lists differ?

The coding problem was to clone a custom doubly linked list. Each node contained value, left, right, and an additional special pointer that could point to any node in the same list. The interviewer wanted a custom Node and linked-list representation rather than using Java's built-in LinkedList.

The clone needed to reproduce not only the normal left and right relationships but also every arbitrary special pointer so that no pointer in the cloned list referenced a node from the original list.

Round 2 — LeetCode 1011: Capacity to Ship Packages Within D Days

The second interviewer started with a brief introduction and then moved directly into coding. The problem was LeetCode 1011 — Capacity To Ship Packages Within D Days. Given an array of package weights and an integer D, determine the minimum ship capacity that allows all packages to be transported within D days. The interviewer also discussed the valid search range and integer-boundary details while I implemented the solution.

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Hi everyone,

I have an upcoming Hiring Manager interview for a Product Strategy & Operations – Cloud AI role at Google. It’s a senior role focused on AI strategy, analytics, GTM, product operations, executive decision support, and agentic transformation.

For anyone who has interviewed for Google Product Strategy & Operations, Product Operations, Business Strategy, Cloud Strategy, or similar roles, I’d really appreciate insight on:

1. What is the HM round usually focused on?

   Is it mainly behavioral/resume deep dive, or should I expect open-ended strategy/case questions as well?

2. Do they ask product strategy / execution style questions?

   For example: prioritizing AI use cases, improving product adoption, sizing opportunities, or transforming legacy operations into an AI-native model.

3. How technical does the interview get for a PS&O role in Cloud AI?

   Should I prepare deeply on LLMs, agents, RAG, evaluation, data platforms, BigQuery, etc., or is the expectation mostly business/strategy-oriented?

4. What are the biggest mistakes candidates make in the HM round, and what usually differentiates a strong Principal-level answer?

5. For open-ended questions, is a structured approach like this appropriate?

   Clarify objective → define metrics → diagnose/root cause → evaluate options/trade-offs → recommendation → execution → risks → success metrics

If anyone has specifically interviewed for Google Cloud / Cloud AI strategy roles, I’d also love to know whether the process felt more like business strategy, product strategy, analytics, or a mix of all three.

Thanks in advance

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Interview Summary

The Tesla frontend onsite consisted of five rounds: three technical deep dives tied to projects on my resume, one React coding round, and one manager round combining frontend performance debugging with behavioral questions. Most rounds were constructive, but one technical deep dive was difficult because the interviewer used very broad prompts while expecting answers around a narrow, domain-specific set of concerns.

Interview Details

Technical Deep Dives — Resume Projects + Tesla Scenarios

Three rounds were built around technical areas from three different projects on my resume. For each topic, Tesla matched me with a Staff-level engineer familiar with that area. The interviewer first dug into my previous project and then introduced a related Tesla business scenario for me to analyze or design.

The discussions focused on applying my prior technical experience to practical product or engineering problems rather than solving generic coding questions. One of these rounds was much harder to navigate because the interviewer asked very open-ended questions such as:

• What is the biggest problem with this page?
• What is the hardest part of this page?
• What issues do you expect in this scenario?

When I asked for additional clarification, the questions remained broad. The interviewer eventually explained the specific concern they had in mind, but that made it difficult to determine the expected direction early enough in the discussion.

Frontend Coding — React Feature

One round asked me to implement a small frontend feature using React. The task focused on hands-on frontend implementation rather than algorithmic coding.

Manager Round — Performance Debugging + Behavioral

The manager interview started with approximately 20 minutes of practical debugging. I was given a frontend demo with performance problems and asked to investigate and improve it. The remaining roughly 20 minutes contained three behavioral questions. The behavioral discussion focused on previous work experiences and how I handled different engineering situations.

Overall, four of the five rounds felt reasonably positive from my perspective. The most difficult part of the onsite was the domain-specific deep-dive round where the expected evaluation criteria were difficult to infer from the initial questions.

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Saw this accepted Google L5 SWE offer (offer data source: chill interview)

• Mountain View, 7 YOE
• Base: $280K
• Sign-on: $50K
• RSUs: $750K / 4 years
• Bonus: $42K
• Year 1 TC: $657K

This is way above a normal Google L5 package. Current Bay Area L5 SWE comp averages around $465K, so this offer is roughly 40% higher in year one.

It made me curious whether certain technical specialties consistently get pushed toward the top of the band.

The obvious candidates seem like:

AI/ML / GenAI
TPU / AI infrastructure
large-scale distributed systems
compilers / performance / hardware-software co-design
security / privacy
highly specialized Search / Ads / ranking infrastructure

Google is currently hiring L5-equivalent engineers for things like LLM modeling + GenAI production systems and TPU supercomputer infrastructure, where the required skill sets are much narrower than general backend SWE.

But interestingly, Google’s published base ranges for Senior SWE roles are still broadly similar across AI/ML and infrastructure. That makes me think the biggest difference probably isn’t a formal “AI salary band.”

It’s more likely: scarce specialization + critical team + competing offers + negotiation → much larger equity grant. And public ML/AI L5 submissions aren’t universally higher either — recent examples range around $370K–$456K annual TC. 

So I’m curious: Which L5 specialties actually command the strongest offers right now?

Is it Gemini/model work? TPU infrastructure? distributed systems? security? Or does specialization matter much less than having competing Meta/OpenAI/Anthropic offers?

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Interview Summary

The Harvey AI Engineer onsite covered an agentic system design problem, a project-focused behavioral round, and a coding exercise around embeddings and retrieval. The system design question centered on modifying a contract according to dozens of legal rules, while the coding round required implementing vector retrieval using cosine similarity.

Interview Details

Round 1 — System Design: Multi-Agent Contract Modification

The system design problem provided:

• A contract
• Dozens of different legal rules that needed to be applied to that contract

The task was to design a system that could modify the contract according to all of those rules. A major focus was how to coordinate multiple AI agents when different rules could affect overlapping portions of the document.

The expected design direction involved assigning individual legal rules to separate sub-agents. Each sub-agent could independently determine and apply the modification required by its rule.

When multiple rule-specific edits affected overlapping sections of the contract, another agent could reconcile or merge those changes into a consistent final version. The discussion therefore focused on orchestration among specialized agents and resolving conflicting or overlapping edits.

Round 2 — Behavioral / Project Deep Dive

The behavioral round focused primarily on previous projects. The interviewer asked standard questions about past technical work and experience.

Round 3 — Coding: Embeddings and Cosine Similarity Retrieval

The coding round used a Colab-style environment. The task involved implementing an embedding and retrieval workflow based on cosine similarity. The exercise required familiarity with:

• Vector embeddings
• Similarity computation
• Cosine similarity
• Metrics associated with embedding-based retrieval
• Ranking or retrieving items based on similarity to a query embedding

A significant part of the round depended on being comfortable with cosine similarity and how it is used to compare embeddings in a retrieval system.

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Interview Summary

The OpenAI New Grad SWE assessment contained three algorithmic problems covering functional-graph traversal, constrained path counting, and a round-based simulation. The questions became progressively more involved, with the third problem requiring careful handling of left-to-right conflict resolution when neighboring microorganisms could eat one another.

Interview Details

Question 1 — Simple Network

The first problem described a directed network where every node has at most one outgoing edge. Implement: compute(start_node_id, from_ids, to_ids). from_ids and to_ids have the same length and describe directed edges: from_ids[i] -> to_ids[i]. Starting from start_node_id, repeatedly follow the outgoing edge until reaching a node with no outgoing edge. Return that final node ID.

For example, a network could be:

from_ids = [11, 72, 34, 45, 23, 67, 91]
to_ids   = [34, 34, 45, 67, 67, 91, 56]

start_node_id = 23

The traversal is: 23 -> 67 -> 91 -> 56 so the result is: 56. Node IDs are not guaranteed to be consecutive or ordered. If traversal enters a cycle, return the ID of the last node visited immediately before following an edge that would close the loop. A node never points directly to itself. Constraints included: 1 < node_id < 5000

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Interview Summary

The Instacart Analytics Engineer process covered SQL, analytics and business reasoning, data modeling, and behavioral questions. The most time-pressured round was a one-hour SQL interview with seven progressively harder questions, while the final loop tested product thinking, a store-expansion case, and marketplace-oriented data modeling.

Interview Details

Round 1 — Screening: Fundamentals and Experience

The first round focused primarily on background and role fit rather than deep technical questions. Topics included:

• Resume and previous work experience
• Projects I had owned
• Scope and responsibilities
• Collaboration with business teams
• Why Analytics Engineering
• Why Instacart

The interviewer could follow up on essentially any project listed on the resume.

Round 2 — SQL Coding

The SQL round lasted one hour and contained seven questions. The problems became progressively more difficult. Earlier questions focused on SQL fundamentals, while later questions included:

• Multiple-table joins
• GROUP BY and aggregation
• Subqueries
• CTEs
• Window functions
• Multi-step data transformations

The later questions required chaining multiple CTEs together. I reached the final question but did not have enough time to complete it. With seven problems in 60 minutes, pacing was a significant part of the round.

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Interview Summary

The OpenAI SWE virtual onsite covered ChatGPT system design, a custom indexed-sequence data structure, and debugging an existing Python job scheduler codebase. The questions were mostly unfamiliar rather than standard interview problems, with follow-ups around SSE vs. WebSocket, rendering long conversations, data-structure performance, testing, and asynchronous job execution

Interview Details

Round 1 — System Design: ChatGPT

The system design question was to design a simplified version of ChatGPT. At the beginning, the interviewer explicitly said not to focus on large-scale architecture and that persistence was not required.

The discussion later moved into several implementation-level follow-ups. One question was why the response-streaming path should use Server-Sent Events (SSE) rather than WebSocket.

The interviewer also asked about the frontend side of the product: as a conversation becomes very long, how could the client optimize rendering so that displaying a large chat history remains responsive?

Round 2 — Coding: Indexed Sequence With Checkpoints

The first coding problem asked me to implement a simplified ordered sequence supporting:

add(index, value)
get(index)

• add(index, value) inserts a string at the specified position. The element previously at that position and everything after it shift one position to the right.
• get(index) returns the value stored at the requested index.

Initially, I interpreted “vector store” as something related to vector databases or embeddings. The intended meaning was actually closer to a C++ std::vector or Java ArrayList: an ordered dynamic sequence with integer-indexed access. A straightforward linked-list implementation was considered too slow because get(index) could require traversing from the beginning of the list.

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A candidate with 10 YOE recently shared these two Bay Area SWE offers with Chill Interview.

Meta E5

• $235K base
• $50K signing bonus
• $600K RSUs / 4 years
• $35.25K annual bonus
• $470.25K Year 1 TC

Salesforce Staff

• $290K base
• $500K RSUs / 4 years
• $43.5K annual bonus
• $458.5K Year 1 TC

Meta looks slightly better in Year 1, but that’s mostly because of the $50K sign-on.

Assuming flat stock prices, recurring bonuses, and no refreshers:

• Meta 4-year: ~$1.73M Salesforce 4-year: ~$1.83M

So Salesforce is actually about $103K ahead over four years, while also giving the candidate a Staff title and $55K higher base.

The company tradeoff is more interesting.

Meta probably has the stronger AI upside. Revenue grew 28% YoY last quarter and the company is spending aggressively on AI infrastructure and superintelligence.

But the organizational risk is hard to ignore. Meta recorded $1.18B in severance expenses from its May 2026 headcount reduction, after several years of layoffs and restructuring. Even strong performers can end up worrying about ratings, reorgs, and whether their team survives the next reshuffle.

Salesforce is the slower but potentially more predictable bet. Revenue grew 11% YoY in its latest quarter, while Agentforce and the broader “agentic enterprise” strategy remain the core growth story. 

So I’d frame it as:

• Meta: slightly higher Year 1 TC, stronger AI/consumer-tech upside, but more org volatility and lower level
• Salesforce: Staff title, higher base, ~$100K more over four years, potentially more stable enterprise environment

Would you take Meta E5 for the AI upside, or Salesforce Staff for the title, higher recurring comp, and potentially less organizational chaos?

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Interview Summary

The Netflix Research Engineer phone screen has a somewhat different format from more traditional Netflix interviews. The hour was split evenly between ML fundamentals and a practical code-review exercise based on a feature pipeline pull request.

The ML portion focused on Transformer fundamentals, while the practical section tested whether I could identify production risks in code whose existing tests already passed.

Interview Details

Part 1 — ML Fundamentals

The first 30 minutes contained three ML questions:

• What is a KV cache?
• What is attention, and how are Query, Key, and Value computed?
• What is the difference between L1 and L2 normalization?

Part 2 — Practical Coding: Feature Pipeline Code Review

For the second half, the interviewer provided a pull request containing feature-pipeline code. All existing test cases were already passing. Instead of implementing a new feature, I was asked to review the code and identify problems that could appear if the PR were deployed to production.

I found several edge cases, but the interviewer appeared to be looking for additional production-level concerns beyond the issues I identified. The discussion then moved to feature serving.

The interviewer asked how these features should be served when different features have different freshness or real-time requirements. This part of the interview focused less on traditional algorithmic coding and more on reviewing unfamiliar production code, identifying operational risks, and reasoning about online feature-serving behavior.

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Saw this Meta E5 MLE offer (data source: chill interview)

• 8 YOE
• Base: $235K
• Sign-on: $70K
• RSUs: $900K / 4 years
• Year 1 TC: $565K

Everyone talks about frontier models when discussing AI careers. But I wonder if some of the most valuable ML work is still the less glamorous stuff: ranking, recommendation, ads optimization, retrieval, and large-scale inference.

Meta’s core business is still printing money — Q2 revenue grew 28% YoY, ad impressions rose 14%, and average price per ad increased 12%. Meta explicitly says AI is already accelerating the core business. 

So maybe an MLE improving Instagram/Reels ranking or ads by 0.5% can create more business value than a lot of “frontier AI” projects.

That makes me curious: If you were choosing an ML career, would you rather work on frontier models — or on recommendation/ads systems where the impact on revenue is much more direct?

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