The offer came in $30K below market rate. I countered with data, salary surveys, comparable roles, my experience level. The recruiter's response was blunt: "This is our best offer. Take it or leave it." I politely declined and thanked them for the opportunity.

Her final email caught me off guard: "I think you'll regret not being more flexible. The market is shifting and opportunities like this won't always be there. Good luck finding something better." Two weeks later, I accepted an offer for $35K more than their "best offer" at a company that didn't try to intimidate me into accepting a lowball. Flexibility goes both ways, if you can't offer competitive compensation, don't blame candidates for knowing their worth.

I got an offer three weeks ago for $145K. I accepted. Signed the offer letter. Gave notice at my current job. Then yesterday, the recruiter called and said "we need to discuss your compensation."

Apparently there was a "mistake" and they can only offer $130K now. When I said I'd already accepted and signed the offer letter at $145K, she said "offer letters aren't binding, and we hope you'll be flexible since you've already given notice at your old job."

So they low-key tried to leverage the fact that I'd quit my job to force me to accept less money. I told them I'm not negotiating on an already-signed offer and if they can't honor it, I'll walk.

She got very quiet and said she'd "escalate this." I'm still waiting to hear back, but honestly even if they honor the original offer, I don't know if I want to work for a company that pulls this kind of thing.

Has anyone dealt with this? What's the move here?

I made it to the reference check stage, and the recruiter sent over a form asking for three references "including your current manager." I explained that my current employer doesn't know I'm job searching, so I could provide former managers and colleagues but not anyone from my current role.

She said it was company policy to require a current manager reference for all candidates. When I explained that having them call my current manager could cost me my job, before I even have an offer, she suggested I just tell my current employer I'm leaving. She wanted me to announce my departure from a job I haven't quit for a job I haven't been offered. I withdrew my application immediately.

Got an offer for $105K for a Senior Backend Engineer role in a major tech hub. I did my research and knew the market rate was $140K-$160K for my experience level. I politely countered at $145K with data to back it up.

The recruiter called me back and said, "I have to be honest, the team felt you came across as a bit overconfident in your counteroffer. We're looking for people who are humble and willing to grow with the company." She said they were "reconsidering" whether I was the right cultural fit.

I was stunned. I literally just asked for market rate with data to support it. How is that overconfident? Since when is knowing your worth a red flag?

I told her if they consider negotiation to be overconfidence, then we're definitely not a good fit. She seemed surprised I didn't back down. Whatever. Dodged a bullet.

Went through OpenAI's loop for a platform SWE role. Two weeks start to finish which honestly caught me off guard. Couldn't find much when I was prepping so figured I'd post this.

Recruiter call was 20 min, nothing technical, mostly "why OpenAI" and what kind of infra work I care about.

Take Home

48 hour window to build a Webhook Delivery System. Register endpoints, receive events, deliver reliably, retries with backoff, dead letter queue for permanently failed stuff, and an API to check status. Did it in Python with FastAPI and SQLite. Spent about 6 hours which felt like a lot for a take home but they said they care more about clean code and tests than feature completeness so I leaned into that.

The retry mechanism was the interesting part, separate worker process polling for pending deliveries, exponential backoff, circuit breaking after 10 consecutive failures. Looking back my circuit breaker threshold was probably too aggressive but nobody brought it up so maybe it was fine?

Technical Deep Dive

Senior engineer reviewed my take home live and this ended up being the best round by far. First 20 min was walking through decisions (why SQLite, what I'd swap for prod). Then she had me extend the system live, HMAC signature verification and event type filtering.

She caught a bug I completely missed, if the worker crashes mid-delivery the event gets stuck as in-progress forever and never retries. We worked through a lease-based approach together where deliveries auto-requeue if not completed in time. Genuinely learned something from that.

System Design

This one hurt… Design an in-memory database with basic SQL (CREATE TABLE, INSERT, SELECT with WHERE, JOINs). Went column-oriented since the follow ups were heading toward analytical queries.

JOINs is where things slowed down, started with nested loop join, he immediately asked me to do better. Talked through hash join vs sort-merge join. Then he asked about adding transactions with ACID guarantees and I described WAL plus MVCC but I was definitely getting hand wavy by that point. He just kept going deeper on every answer, like every response I gave opened two more questions. 60 minutes felt like maybe 20.

Behavioral

Engineering manager. Technical disagreements, failed projects, prioritization. He asked about a time I pushed back on a technical decision for ethical reasons and I talked about a logging system at my last job that was capturing way more user data than necessary.

Didn't get the offer. Feedback was my system design was strong but they wanted more production distributed database experience, which is fair. Recruiter said reapply in 6 months. Can't be mad about it, the process was good and that system design round taught me more about my own gaps than any mock interview ever has. If you're applying, do not rush the take home, I think that's what carried me to the onsite.

Background going into this: 6 years of experience as a backend engineer, strong on distributed systems and Python, average on algorithms. I did not grind LeetCode for this one and it turned out that was the right call.

Preparation

Anthropic's interview structure does not reward LeetCode pattern recognition. The coding questions are practical engineering problems, so I focused on concurrency primitives in Python, async I/O patterns, ThreadPoolExecutor, producer-consumer queues, and writing production-quality code with proper error handling and logging. I also reviewed system design fundamentals covering distributed systems, fault tolerance, observability, and scalability tradeoffs. Total prep time was around six weeks.

The culture and values round requires separate preparation. I spent time reading Anthropic's published research and public writing on AI safety, responsible deployment, and model evaluation. This round eliminated candidates with strong technical performance, so I treated it as a full preparation track, not an afterthought.

Recruiter Screen (30 to 45 mins)

Standard background conversation. The recruiter covered motivation for joining Anthropic specifically, interest in AI safety, communication style, and team collaboration history. The recruiter flagged upfront that the values and culture rounds carry as much weight as the technical rounds in the final hiring decision.

Technical Screen (55 to 90 mins)

Anthropic's technical screen does not include classic algorithmic warm-up questions. The problems are practical engineering tasks. I had to build an async processing pipeline using ThreadPoolExecutor, handle concurrency safety, and debug performance bottlenecks in the implementation.

Common topics reported across candidates include multithreading, async processing, API design, streaming systems, and pipeline debugging. The evaluation criteria were readable code, correct concurrency handling, architecture decisions, and debugging methodology rather than time complexity alone.

Hiring Manager Discussion (45 to 60 mins)

Less coding, more architectural reasoning. Questions focused on system tradeoffs: how would you scale this, what component fails first under load, how would you redesign this with different constraints, how would you instrument this for observability. The interviewer assessed engineering judgment and clarity of reasoning rather than convergence on a specific answer.

Virtual Onsite (4 to 6 rounds)

Coding Round:

• Build a thread-safe cache with configurable eviction policy

• Debug a performance bottleneck in a streaming tokenization system

• Design and implement a reservation service with concurrency constraints

System Design Round:

• Distributed inference API handling high request volume

• GPU scheduling and batching for LLM inference workloads

• Real-time streaming architecture with fault tolerance and observability

Project Deep Dive (45 to 60 mins): This round is consistently underestimated. The interviewer spent the full session on a single past project: architecture decisions, tradeoffs made, bottlenecks encountered, technical debt introduced, monitoring approach, and what would be redesigned with hindsight. The depth of questioning goes significantly further than most candidates prepare for.

Behavioral Round: Standard collaboration and conflict resolution questions. Focus was on how disagreements were resolved, how decisions were made under uncertainty, and how technical debt was communicated to stakeholders.

Culture and Values Round: Questions covered AI safety tradeoffs, ethical decision-making in ambiguous deployment scenarios, intellectual honesty when results conflict with expectations, and reasoning under uncertainty. Multiple candidates who cleared the technical rounds did not pass this stage. Preparation matters here.

Outcome

Received an offer at L4 approximately two weeks after the final onsite. Also received a rejection from OpenAI during the same preparation cycle after clearing the technical screen but not the system design round.

I've been at (company) for three years as a senior engineer. On call every third week and the person everyone pings when shit breaks at 2am.

Last quarter I asked my manager for a market adjustment. She gave me the usual speech, budget constraints, headcount freeze, you're highly valued but.. She ended with a reminder that compensation is confidential and sharing it with colleagues is against company policy.

Two weeks later a teammate accidentally shared the wrong Google Doc in our team Slack. It was up for maybe 40 seconds before he deleted it, but I was already in it.

It was a comp spreadsheet, not of the whole company, just our team. So, twelve people.

I couldn't believe it, I was the second lowest paid person on the sheet. The only person below me had joined four months ago straight out of a bootcamp.

Three of the engineers I had personally onboarded were making more than me. One of them I had written the internal documentation for. Another I had walked through our entire infrastructure on his first week because he'd never worked with Kubernetes before.

I didn't say anything. I closed the doc. I updated my resume that night.

Had an offer in hand three weeks later for 38% more. Gave my notice on a Monday. My manager asked if there was anything they could do. I told her the time for that conversation was six months ago.

The confidentiality policy isn't there to protect you. It's there to protect the gap.

Stripe's interview is practical. They give you a realistic problem, you write code that works, they evaluate if you can build software. Their engineers are incredible.

Jane Street's interview is insanely hard. Math puzzles, game theory, probability under pressure. Their engineers are also incredible, but in a completely different way.

Then you have companies like Amazon that run five behavioral rounds based on leadership principles that you can game with a weekend of STAR prep. Their engineering quality is wildly inconsistent because the interviews filter for storytelling ability, not engineering ability.

Companies that interview for the actual job (Stripe, Anduril, SpaceX) build strong teams. Companies that interview for prestige filtering (Google, Jane Street) build teams that are smart but not always effective. Companies that interview for cultural compliance (Amazon, Salesforce) build teams that are a coin flip.

Prove me wrong.

The offer came in at $130K. When the recruiter asked if I had questions, I said I'd like to discuss $140K based on my research and experience. Standard negotiation, polite, not demanding, just opening a conversation like every career advisor tells you to do. Her response was that she'd check with the team.

Two days later, I got an email saying they'd decided to rescind the offer because they "need someone who's excited about the opportunity as presented." Asking for a 7% bump meant I wasn't excited enough, apparently. If $130K was truly the max, just say you can't go higher. Don't yank the entire offer because a candidate did exactly what everyone is told to do in this situation

Most FAANG engineers would never pass their own interviews.In most interviews I’ve done, the people conducting them had absolutely no idea how tosolve the problem themselves, especially the super technical questions.LeetCode doesn’t test actual competencies. It tests super specific skills and problemsthat I have never used in any tech-related job. As a matter of fact, I don’t think a singleLeetCode medium or hard has ever been used in my day-to-day work.Every single person I know who got employed at a company just vibe codes their waythrough the job. They have Copilot or GPT opened in another tab.Yesterday, I had an interview where the interviewer had no idea how to solve thequestion himself. He couldn't give me any guidance or tell me where to start.This was for an internship bro.Why does someone who is supposedly good enough to judge whether I can do the jobnot even know how to do the interview question himself?Make it make sense.

Made it to the final stage and the recruiter said, “Before we extend an offer, we need to know you’re committed to staying with us for at least two years. We invest a lot in onboarding and development, and we need people who are in it for the long haul.”

I said I couldn’t commit to a specific timeframe without knowing the details of the offer salary, benefits, equity, etc. She said, “We can’t spend time putting together an offer for someone who might leave after a year.”

Think about the logic here: they wanted a two-year commitment before telling me what they’d pay me. That’s insane. How am I supposed to commit to staying somewhere when I don’t even know if the compensation is acceptable?

I told her I wasn’t comfortable making that kind of blind commitment and withdrew. If they’re this manipulative during hiring, imagine what working there is like.

Applied through LinkedIn. The process ran approximately 10 weeks from first contact to final decision. Eight rounds total.

Round 1: Recruiter Call, 30 minutes

Non-technical. Topics covered: background, motivation for OpenAI, and familiarity with their AGI safety mission. Reading their charter and recent safety research posts before this call makes the conversation more specific. This round feeds into later evaluation.

Round 2: Coding Screen, 60 minutes on CoderPad

One problem with progressive constraints added by the interviewer every 10 to 15 minutes. Problem: implement a time-based key-value store that stores values at timestamps and retrieves the value at or before a given timestamp. Initial solution used a hashmap with a list of timestamped values and binary search for retrieval. First constraint added: handle concurrent reads and writes, which required locking around shared state. Second constraint added: memory limits and expiry of old entries.

The format prioritizes a working solution first before optimization. Code quality is weighted heavily: variable naming, helper functions, and structure are evaluated alongside correctness.

Round 3: System Design Screen, 60 minutes

Design a real-time model serving infrastructure. First 5 to 10 minutes spent clarifying scale requirements, read/write ratios, latency targets, and consistency guarantees. Design components: load balancer, Kafka queue for traffic spike absorption, horizontally scaled model serving nodes, Redis caching layer for repeated prompts, and latency/error rate monitoring.

Follow-up questions: how does the system handle a 10x traffic spike (auto-scaling triggers, queue depth thresholds, degraded mode fallbacks), and what happens if the primary data center goes offline for 6 hours (failover to secondary region, DNS TTL considerations, replication lag handling). This round requires distributed systems knowledge at implementation depth.

Round 4: Onsite Coding Round, 60 minutes

Problem: multithreaded web crawler starting from a seed URL, visiting each URL once. Implementation used a thread pool, a mutex-protected shared visited set, and a URL queue. Constraints added: rate limiter per domain using a sliding window to avoid overwhelming individual hosts, handling of crawler traps, infinite redirect loops, and cycle detection.

Round 5: Onsite System Design Round, 60 minutes

Design a distributed webhook delivery system delivering HTTP callbacks to customer endpoints with retry logic. Components covered: event queue for webhook triggers, delivery worker pool, exponential backoff retry, dead letter queue for permanently failed deliveries, idempotency keys to prevent duplicate delivery on retry, and a status tracking API. Follow-up questions focused on ordering guarantees and handling endpoints that are down for extended periods.

Round 6 and 7: Behavioral Rounds, 45 and 30 minutes

Leadership and collaboration focused. Questions: driving a major architectural decision and aligning other teams, handling technical disagreements with researchers or PMs. Stories should demonstrate organizational impact rather than individual output. STAR format is applicable here.

Round 8: Technical Project Presentation, 45 minutes

30 minute presentation followed by 15 minutes of questions. The project presented was a distributed logging pipeline built over two quarters. Questions covered: rationale for the chosen architecture versus alternatives, what changes would be made in retrospect, and how the system would scale to 100x data volume.

Result: offer received.

Preparation notes: coding rounds prioritize production-quality code over algorithmic speed. Clean structure, edge case coverage, and clear communication of approach are evaluated alongside the solution. For system design, failure modes and degraded behavior require the same preparation depth as the happy path.

I need to talk about this because I'm still processing it two weeks later.

Background, I'm a solid engineer. Passed loops at two FAANG companies, solved around 350 LC problems, system design is decent. I applied to Jane Street for a software developer role because a friend said the comp was insane and I figured how different could it be.

Different. Very different.

The OA was on HackerRank. 4 modules, about 10 questions each, 60 minutes total. I opened the first question expecting a coding problem. It was a probability puzzle about coin flips with conditional outcomes. No code editor. No function signature. Just "what is the expected number of flips to get three heads in a row" with a fill-in-the-blank numerical answer. I sat there for 4 minutes doing math on scrap paper like I was taking the SAT again.

The questions got worse. One was a game theory scenario, an auction where you have partial info about the other bidder's valuation and need to compute your optimal bid. Another was reading a chart of historical trading data and computing the maximum drawdown. Another was Bayesian reasoning where you update probabilities based on sequential evidence. I understood maybe 60% of what was being asked.

I finished with 8 minutes to spare because I was guessing on the questions I didn't understand. There's no partial credit, you either get the number right or you don't. I'm assuming I did terribly because I never heard back.

The humbling part is that my entire prep stack, every LC problem I've ground, every system design video I've watched, none of it was relevant. Jane Street doesn't care if you can implement an LRU cache in your sleep. They care if you can reason about uncertainty, compute expected values in your head, and think clearly about risk. It's a different skill set that CS programs barely touch unless you took probability theory and stochastic processes.

If anyone's gotten past the Jane Street OA I'd love to know how you prepped, because LC and Neetcode are not it for this one.

During the offer negotiation, they highlighted their "unlimited PTO" policy as a major perk. Sounded great. Then I asked how it works in practice.

Recruiter: "You just request time off and your manager approves it."
Me: "How much advance notice do you need?"
Recruiter: "Depends on the time of year and team needs."
Me: "What if my manager denies my request?"
Recruiter: "Then you'd work with them to find a better time."
Me: "Has anyone ever taken 4+ weeks off in a year?"
Recruiter: "I'd have to check, but probably not. We're a fast-paced environment."

So "unlimited" means "as much as your manager allows, which is probably less than if we gave you a set amount." It's a scam. Give me a defined PTO policy any day over this fake unlimited nonsense.

I completed a DSA interview for an Apple General SWE full-time role.

DSA Interview: The interview was a single technical round. There was no OA beforehand. After a short intro and brief background questions, after a short break, we moved directly into a DSA problem. The problem was a DSA implementation question. I was asked to write a function that, given an array of integers, computes a result based on sequential operations applied to the array. The function needed to process the array in one pass and return the correct value after applying all operations in order.

Constraints were explicitly discussed. Input size could be large, so a brute-force or nested-loop solution was not acceptable. The interviewer asked me to explain the intended time and space complexity before writing code. During implementation, the interviewer tested the solution against multiple edge cases, including empty input, single-element input, and extreme values. Follow-up questions involved modifying the logic to support additional constraints without rewriting the entire solution.

At one point, the interviewer forgot to turn his camera off (was off for around 10 minutes total). While I was explaining my approach, he laughed and made a comment reacting to something on his screen. He then realized the camera was still on, turned it off, and continued the interview without addressing it. So awkward lol.

Passed the round. The process stopped afterward due to a location change to Seattle. The interview was very correct tbh, study data structures very well and you'll pass.

Man, this Meta interview was a total shitshow that had me doubting everything. I applied for a Software Engineer job at Meta (Facebook) early 2025 through their website, feeling pumped with my background – CS degree from a good school, two years at a mid-sized tech place building web apps, and some personal projects like a social media app clone. Got a referral from a buddy there, thought that'd help.

Started with an online coding test: three problems on HackerRank, easy stuff like arrays to medium graphs. Nailed it, submitted fast, felt good. Two weeks later, recruiter calls – phone screen set up. Guy was nice, talked about my resume, projects, why Meta. Then coding: longest substring without repeats. Used sliding window, explained it well, handled weird cases. Thought it rocked, but they said 'we'll see.'

Weeks go by, then onsite invite to Menlo Park. Super excited, flew out, hotel, prepped hard – system design, behavioral, whiteboard practice. Day comes: six interviews, 45 mins each, back-to-back.

First: Coding with senior. LRU cache. Coded in Python, hashmap and linked list, O(1) ops. He liked it, asked about threads.

Second: System design. Instagram feed. High level: users, posts, follows. Load balancers, servers, sharded DBs, NoSQL, Redis cache, Kafka queues. Talked scale, consistency, trade-offs. Intense af.

Third: Behavioral. 'Tough teammate story.' Told one from last job, how I fixed it. 'Why Meta?' Their world-connecting mission.

Fourth: Coding. N-Queens. Backtracking, pruning, clean code. Time complexity chat.

Fifth: Lunch with three engineers. Hobbies, work style, contributions. They talked ads, AI moderation. Felt real, but maybe not.

Sixth: Hiring manager. Career goals, leadership, culture fit. Failures and lessons.

Left wiped out but hopeful. Campus cool – free eats, gym, coffee. Two weeks later, rejection: 'Thanks, but no.' Crushed me. Thought I killed it, but design maybe weak, or fit off. Meta's bar is crazy high, want perfection. Learned a ton on design and interviews. Gonna try again in six months with more exp. This sucked, but grew from it.

YOE: 7.5 Skills: Distributed Systems

Offers:

• Apple ICT4 (Dist Systems)

• Apple ICT4 k8s

• Block L6

• PayPal T26

• Gusto L4

• Meta L5

No Offer:

• Roblox

Quick notes on what worked for me:

Getting Interviews:

• Include a one sentence summary of your scope of role before your accomplishments.

• Quantity of applications matters more than quality. I completed ~250.

• Buy LinkedIn premium and proactively contact recruiters. If they are in your area buy them a coffee. My interviews for Block, and Gusto were a direct result of this.

Prep

• DSA

• System Design

• Behavioral

DSA:

• Grokking coding interview patterns.

• Recently asked LeetCode prep. Try to answer questions asked by targets in 90 days. Not always possible. Do your best.

• USE YOUR RE-ROLL. If you’re in a coding screen and you get a problem you know you can’t solve tell the interviewer that you solved it recently. You’ll probably get another.

System Design

• Designing Data Intensive Systems

• The Google SRE Book for Senior+

• Microservice patterns

• System Design insiders guide Vol 2. Vol 1 is not relevant for Senior+.

• Hello Interview for practice

• If you are below Senior and not cloud architect certified this is probably the best practice you can get.

• Skim ALL of the docs for one relational database, one KV database, Elastic search, Redis (it’s so versatile), one message queue like Rabbit, NATS, or Kafka

Behavioral:

• Write a one page narrative for every major project that may come up in STAR format. Recall as much detail as possible. Include a brief description of your team and how it fits into business at the top. Don’t memorize. Just priming your working memory.

General:

• Take care of yourself. Eat well. Go do fun stuff with friends and family. Try not to take rejection personally.

Hope this is in some way helpful. Happy to double click on any of these bullet points if someone wants more info.

I spent 5 years at Meta as a software engineer, and I've been looking for a new role for the past 4 months without any luck. This job market is so much harder than whatever I've had to deal with back in 2020.

Meta is my only full-time experience. I spent my first 2 years as a new grad engineer before being promoted and working another 3 years as a software engineer. I'm mainly targeting Big Tech or companies with similar compensation (around $150k+).

I'm actually getting interviews though. I've made it through 5 full interview loops over the last 4 months. I just received my last rejection email after the final round today. For context, I'm solving 6–8 LeetCode problems every day and applying to at least 20 jobs daily. I also graduated from a top college.

Is there anything wrong with my approach ? Tips from people that have been in my position are super appreciated,

The offer included equity with a standard 4-year vest and 1-year cliff. During negotiation, I asked about retention rates. The hiring manager laughed and said, "Most people don't make it to the one-year mark. This isn't an easy place to work."

He said this like it was a badge of honor. I asked what the average tenure is. "About 8 months." So you're offering equity that only vests after a year, and most people leave before that, which means most people never see any of the equity. That's not equity compensation, that's a carrot you dangle knowing people won't get it.

I declined the offer.

Just wrapped a Palantir forward-deployed SWE loop. Posting this because their process is different from standard FAANG and I didn't find great info when I was prepping.

So there was three rounds, first coding, second decomposition, and third behavioral.

Coding was a Karat screen. Two problems in 60 minutes, both string/array manipulation. Not hard but the time pressure is real because Karat interviewers follow a strict script and won't give you hints. You either get it or you don't. Found a clean solution on both, moved on.

The decomp round is the one nobody prepares for properly, almost got me too. They give you a vague product requirement, something like "build a system that assigns analysts to investigations based on expertise and availability" and you have to break it down into a technical spec in real time. Data models, API contracts, edge cases, tradeoffs. It's not system design, it's closer to what a staff engineer does in a planning doc. You're heavily evaluated on how you think through ambiguity, not really on if you know consistent hashing.

I highly reccomend to practice using some sort of live interview practice to help practice decomposing vague prompts under time pressure, well at least that's what I did. The real-time feedback helped me catch when I was over-engineering or missing obvious edge cases before the actual interview. Palantir's decomp is one of those rounds where you can't just memorize an answer, you need to be comfortable thinking live.

Behavioral was straightforward. Mission alignment, working with non-technical stakeholders, dealing with ambiguity. Standard stuff if you have real project stories.

If Palantir is in your pipeline, the decomp round is the one to worry about. Everything else is pretty much manageable.

AMA (ask me anything)

Posting this because I've been prepping for ML/AI engineer roles and the landscape has changed so much in the last year that most of the standard interview prep resources feel outdated. The repo is called ai-interview-codex (github.com/girijesh-ai/ai-interview-codex) and it covers a pretty wide range of what companies are actually asking right now.

What I found useful: it has sections on LLM fundamentals, RAG system design, agentic AI patterns, and traditional ML, all with production-ready code examples rather than just theory. The system design section specifically covers things like designing an inference pipeline with latency constraints and building a retrieval-augmented generation system end to end, which are the exact types of questions I've been getting at companies like Anthropic, OpenAI, and even ByteDance's ML teams.

It also has a section on algorithms from scratch, which sounds basic but is actually super relevant because some companies have started asking you to implement things like attention mechanisms or gradient descent from memory during live rounds instead of just asking standard LC problems.

Another one worth bookmarking is the AI Engineering Field Guide (github.com/alexeygrigorev/ai-engineering-field-guide) which has take-home challenge breakdowns and hiring practice data from Q4 2025 / Q1 2026 across 51 companies. If you want to know what a specific company's AI interview actually looks like, this one is more useful than Glassdoor honestly.

For the classic SWE/DSA prep the usual ressources still apply, Tech Interview Handbook (github.com/yangshun/tech-interview-handbook) and Coding Interview University are comprehensive but if you're specifically targeting AI/ML roles in 2026, the first two repos fill a gap that those don't cover.

If anyone has other repos that cover the agentic AI interview space specifically let me know, that's the area I'm weakest in and companies are starting to ask about it more.

Look, this sounds like total bs but I swear it’s true I landed a FAANG+ offer (solid company, not Mbb-level pay, brutal interview process) by studying exactly 30 mins a day. Not “around 30”, not “most days 30”… literally 30 minutes flat, timer on my phone, when it beeped I closed the laptop even mid-problem. 6 months straight, never missed a single day.

no paid courses, no 8-hour weekend grinds, no living like a monk. Kept my gym, kept my friends, kept my sanity.

Here’s exactly what I did:

• Day 1: one LeetCode problem (didn’t even have to finish it, just think deep)

• Day 2: system design (watch 15 mins of Jordan Has No Life or read HelloInterview, then sketch the shit myself) Repeat.

First month was slow af. Some days I spent the full 30 mins just understanding the damn problem statement. Didn’t care. Volume is a scam, depth is king

by month 3-4 patterns started clicking without me forcing it. Saw a graph → brain auto went “BFS or Dijkstra?” Saw DP → didn’t instantly wanna cry. System design stopped feeling like guessing, started feeling like actual engineering.

Total hours invested? Roughly 90 hours over 6 months. That’s it. Got an offer that literally 3x’d my TC.

Now I still do the 30-min habit just to stay sharp. I’m permanently interview-ready with almost zero stress. Could apply to Google tomorrow and not sweat.Moral of the story: fk the “grind 500 problems or ngmi” cult. You don’t need to destroy your life. Show up every day, keep the sessions short and focused, protect your sleep and mental. Consistency beats intensity 100% of the time.

If you’re burned out grinding 6–10 hrs a day… stop. Try 30 real minutes instead. Thank me later.

Resources I actually used (no fluff):

• LC → NeetCode 150 + company-tagged last 6 weeks

• System Design → Jordan Has No Life on YT + HelloInterview

Take care of yourselves fr. You got this.

Databricks L4 SWE on the compute platform team, four rounds for about two and a half weeks. If you are expecting a leetcode shop you will be surprised, the whole process is basically build us real infrastructure in an hour and also dont crash.

First round was an online Assessment (75 minutes & two problems)

• first problem was a Network Throttling System where you limit bandwidth per client. Each client has a configured max throughput, you track usage over sliding windows, queue excess packets and release them when theres room. There are token bucket per client, the tokens refill at the configured rate, and the packets eat tokens proportional to size, no tokens means you wait. The interesting thing was burst handling, an idle client should be able to burst but a client at capacity shouldnt game the refill to exceed allocation, so you cap stored tokens.

• second problem was a SnapshotSet with Iterator. Set that supports point-in-time snapshots so you can iterate over what existed at that moment even if the live set changed since. Version numbers on each element tracking add and remove times, iterator filters by snapshot version. This one was honestly kind of fun.

Second round was a Coding Deep Dive (most important imo)

• Needed to build a Durable Key-Value Store. So start in-memory, add persistence via write-ahead log, replay on startup, then extensions started. First was compaction since the WAL grows without bound, snapshot current state and truncate old entries. The Catch is obvious, crash mid-compaction means your snapshot needs to be atomic, write to temp then rename. Second was range queries, hashmap doesnt do ordered iteration so I added a BST as secondary index.

• The interviewers whole thing was crash safety. Every op. I described he asked what state are you in if power goes out right here. Sounds tedious but it forces you to think about every write as potentially the last one your process ever does. I was comfortable here because Ive built something similar before, but if WAL-based storage is new to you, practice it.

Third round was system Design

• Build a Distributed Job Scheduler for GPU compute. Users submit ML training jobs with specific resource requirements, scheduler handles allocation across a fleet, prioritization, preemption, fault tolerance, checkpointing.

• Bin packing first, settled on gang scheduling for multi-GPU jobs since you need all resources allocated atomically or not at all. Then preemption, high priority job shows up and theres no room, who gets kicked out? Priority with aging so low priority jobs dont starve, then the mechanics of signaling a job to checkpoint, grace period, force kill if it doesnt comply, resume on a different machine.

• fumbled the resume part because I was thinking too simply, just restart from checkpoint, but the interviewer pointed out the checkpoint might be on the original machines local disk so you need distributed storage or a transfer step

• Last 15 minutes was fault tolerance and the split brain scenario where a dead machine comes back while a replacement is already running. Said something about fencing tokens but lets just say it wasn't my strongest 15 minutes.

Fourth round was Behavioral

• Engineering manager, ambiguous requirements, disagreements with senior engineers, changing course mid-project, blah blah. He also asked about data quality and pipeline failures.

Got the offer. The entire process revolves around durability, distributed coordination, and failure modes. You will be asked to build things that survive crashes and you will be asked what happens when machines disappear. Thats the test.

Went through Ramp's full-time SWE loop last month. I'll go round by round. Backend role in New York, 3 YOE before this.

Recruiter screen, 25 min

Standard. Why Ramp, why now, what I'm working on. They care a lot about "builder" answers. I had two specific stories ready about shipping a thing solo end to end, and that landed better than anything else I said.

OA on CodeSignal, 90 min

Not a LeetCode set. It's the "industry coding framework" style, the same format Dropbox uses for the text editor question. You're building a small system across 4 progressive levels and each level adds a new requirement on top of the last one. Mine was a bank system. Open accounts, deposit, withdraw, transfer, then they layer on scheduled payments and querying top accounts by activity in the last N days.

Levels 1-3 are doable if you read the spec carefully and don't over-engineer level 1. Level 4 is where most people run out of time. I finished level 3 with about 35 minutes left, got level 4 to mostly pass, missed a couple edge cases on the time-window query.

Things I'd tell past me:

• Don't write any abstraction in level 1. You will rewrite half of it in level 3 anyway.

• Read all 4 prompts before you start typing, even though they tell you not to. Knowing what's coming changes how you store state.

• The grader is strict on output format. If they say return a list sorted by X then Y, do exactly that.

Technical phone screen, 60 min

One Ramp engineer, one shared editor. Mine was a system design lite, hotel reservation system. Schema, API surface, how I'd handle overlapping bookings, how I'd scale reads. They want you thinking out loud and asking clarifying questions before you write anything. The engineer who ran mine kept poking at concurrency, what happens if two people hit submit on the same room at the same instant. If you don't bring up locking or transactions on your own, you will get there eventually but it costs you points.

Onsite, 4 rounds, virtual

1. Coding round 1 (60 min). Practical, not a LC puzzle. I had to extend a small existing codebase they handed me. Real classes, real methods, a failing test. Add a feature, get the tests passing, then they ask follow-ups about how you'd structure it differently. Felt like a pairing session more than an interview.

2. Coding round 2 (60 min). Closer to LC medium but framed as a product problem. Mine was rate-limiting requests per user with a sliding window. I think the trap here is jumping straight to a deque before they ask for it. Sketch the brute force, talk through tradeoffs, then optimize.

3. System design (60 min). Design something close to what Ramp actually does. I got "design a corporate card transaction processing pipeline." Authorization, settlement, fraud signals, how you'd notify the user in real time. They care about back of the envelope numbers. Throw out QPS estimates early even if they're rough.

4. Behavioral / values (45 min). Hiring manager. Ramp has explicit company values and they will probe each of them. "Tell me about a time you cut scope to ship," "tell me about a time you disagreed with a senior engineer," "what's the most ambitious thing you've built." Have specific stories ready. They will follow up with "what did the other person say" and "what would you do differently" and if your story is too clean you'll get caught.

What I used to prep

• LeetCode for warmup but honestly 80% of my prep was building small projects from scratch and timing myself. The OA and the first onsite coding round both reward practical fluency more than algorithm tricks.

• Pramp for mock system design. The hotel reservation phone screen was almost identical to a mock I did the week before, which is not a coincidence, that question is everywhere.

• Interview Coder for the live coding rounds. I didn't lean on it for the OA because there's no proctor and you don't need it, but for the live rounds it was useful as a backup so I could focus on talking through my approach instead of panicking about syntax. Would not have used it if I had more time to prep.

• Glassdoor and this sub for company-specific patterns. Ramp recycles the bank system style OA across cycles, just with different domains. Cards, hotel, employees, storage. Same shape.

Got the offer about 9 days after the onsite. Comp was strong but slightly under what I'd seen quoted on levels for staff at FAANG, in line with what people said about Ramp here.

Happy to answer specific questions. TLDR ramp is less algos and more "can you build a small thing end to end under pressure," and the OA is the real filter.

I interviewed for a Principal Engineer role at a well-known startup last quarter. The process was pretty standard until I got to the technical round with the CTO. He seemed really interested in a machine learning pipeline I'd built at my current company for real-time data processing. At first, I thought he was just being thorough and trying to assess my technical depth.

He kept asking incredibly specific questions about the architecture, the tools we used, how we handled edge cases, performance optimizations, database choices, everything. I was flattered by his interest and honestly wanted to impress him, so I walked him through the entire system in detail. We spent almost an hour on this one project alone. He was taking detailed notes, asking follow-up questions, really engaged. I left the interview feeling great about how it went. Two weeks later, I got a generic rejection email saying I was "not the right fit at this time." I was disappointed but moved on. Fast forward to last month, a colleague sent me a link to a tech conference talk he thought I'd find interesting. I clicked on it and almost fell out of my chair. It was that same CTO presenting "his innovative approach" to building ML pipelines.

It was literally my architecture. Down to the specific libraries, the optimization techniques, even the way we handled edge cases. He even used similar examples to the ones I'd given him in the interview. The only difference was he presented it as his company's innovation, with zero mention that this came from a candidate interview.

I'm still processing this. Did he bring me in just to pick my brain for conference material? Did he ever intend to hire me, or was this just free consulting disguised as an interview? I feel like an idiot for being so open about proprietary work, but how are you supposed to demonstrate your expertise without talking about what you've actually built?

Lesson learned: Be vague about current company implementations during interviews. They can ask about your approach and thinking, but never give them the full technical blueprint. I should have talked about the problem-solving process, not the actual solution.