Do FAANG Companies Still Ask LeetCode? (2026)

Direct Answer Up Front

Every FAANG company still asks LeetCode-style coding rounds in 2026. Meta, Google, Amazon, Apple, and Netflix all run algorithmic coding interviews as a core part of the loop, with the format weighted toward medium-difficulty problems and substantial follow-up depth. The changes since 2023 have been about probing depth and AI-aware adjustments, including an AI-enabled coding format that Meta has rolled out and Google is gradually piloting. Candidates who believed the LC format would collapse under the weight of widely available AI tools have been wrong. The bar has risen, the rounds have become more conversational, and the LeetCode pattern set remains the canonical preparation surface.

The Death-Of-LeetCode Myth That Did Not Happen

The recurring prediction from 2023 onward held that AI coding assistants would force FAANG to abandon LeetCode-style interviews. Two years later, that prediction has not played out. Meta still runs two algorithmic coding rounds per loop. Google still runs four to five technical interviews built around coding problems. Amazon still runs two to three coding rounds plus a system design round and a Bar Raiser. Apple's variance by team is large but every team still includes coding. Netflix still includes coding inside its discussion-heavy panel.

What changed is not the format but the texture. Question selection has tilted toward problems with deeper follow-ups, more emphasis on edge cases the candidate must surface unprompted, more probing on trade-offs, and more conversational evaluation of reasoning quality. Speed has dropped as a signal because speed is the thing AI tools genuinely change. Reasoning depth has risen because reasoning is the thing AI tools change least. The LC pattern set still matters, but the rounds now reward candidates who can articulate why a pattern fits, not candidates who pattern-match faster than the next applicant.

The companies have also launched two experiments. Meta now runs an AI-enabled coding round where candidates use approved AI tools on one of the two coding interviews, evaluated on decomposition, prompt design, output verification, and code quality at a problem complexity exceeding 100 lines. Google is rolling out a similar AI-permitted format gradually. Both experiments coexist with traditional LC rounds for the foreseeable 12 to 18 months and do not replace them.

Company-By-Company Breakdown

The shape of the LC ask varies meaningfully across the five companies. The variance has narrowed since 2023 in terms of difficulty level but widened in terms of round structure and follow-up style. Each company gets a separate treatment below.

Meta: Two Rounds, Strict Time Budget, Heavy Follow-Up Weight

Meta runs two coding rounds in the standard loop, both inside CoderPad, both with the same structural template: two problems in 35 to 40 minutes, roughly 17 minutes per problem including clarification, approach, code, and trace. The internal budget is tight enough that running over on the first problem creates immediate pressure on the second. Question selection sits squarely on Meta's tagged LeetCode list. Tree, string, graph, and DP problems dominate. Meta's top eight problems share almost no overlap with Amazon's or Google's most-asked lists; the lean toward Binary Tree Vertical Order Traversal, LCA III, Right Side View, Minimum Remove to Make Valid Parentheses, and Basic Calculator II is a durable pattern.

The new wrinkle is the AI-enabled round Meta has rolled into a portion of the loop. The format gives the candidate one classic LeetCode-style problem with no AI, and one AI-enabled round where approved tools are available. The AI-enabled problem is harder than a medium, typically requiring 100 lines of code or more, and is designed assuming AI assistance is in play. The evaluation criteria are problem-solving, code quality, and verification of AI output, not prompt engineering virtuosity. Candidates who use the AI as a typing accelerator on a problem they understand do well. Candidates who hand the problem to the model and accept the output without verification fail.

Google: Four To Five Rounds, Algorithm-Heavy, Googley Follow-Ups

Google's loop still runs four to five technical rounds, each 45 to 60 minutes, each centered on a coding problem in a shared editor or doc. The question pool leans heavier on graphs, recursion, and dynamic programming than Meta's. The follow-ups are characteristic: an interviewer will solve the problem with the candidate, then extend the problem into territory that breaks the original solution, then ask the candidate to adapt. The signature move is the second extension after the first one, which forces the candidate to redesign rather than tweak. This pattern is sometimes called the Googley follow-up because it tests the candidate's ability to keep reasoning past the point where rote pattern-matching helps.

Google's AI-permitted coding round is rolling out gradually through 2026 and is not yet standardized across the loop. Most candidates encounter traditional rounds. The candidates who see the new format report problems that look more like small implementation tasks than puzzle-style LC questions, with the evaluation focused on prompt design, AI output evaluation, decomposition, and explicit reasoning. The transition is expected to take 12 to 18 months to standardize. Until it does, LC-style preparation remains the dominant useful surface for Google, with the caveat that pure speed-grinding helps less than it used to. The deeper treatment of Google's loop structure and the specific problem patterns is in how to pass a technical interview at FAANG and hardest LeetCode questions asked in FAANG interviews.

Amazon: Two To Three Coding Rounds, LC Medium, Bar Raiser Looms

Amazon's process in 2026 still includes two to three coding rounds of 45 to 60 minutes each, primarily LeetCode-medium problems on arrays, strings, sliding windows, and basic tree traversals. The funnel begins with an HackerRank-based online assessment for new grads and SDE I/II roles: 90 to 120 minutes, two algorithmic challenges, almost always mediums. Pass rate on the OA is the first hard filter. The phone screens and on-site loop add the rest of the coding evaluation plus a system design round and a behavioral-heavy Bar Raiser.

The Bar Raiser is a specially trained interviewer from outside the candidate's potential team who joins the loop specifically to enforce hiring consistency. The Bar Raiser round mixes coding and behavioral probes, with the explicit goal of ensuring the candidate raises the bar relative to existing engineers at the target level. Leadership Principles weight is roughly half the total evaluation, which means a candidate can pass every coding round and still fail the loop on weak LP answers. Amazon has leaned into LP weight slightly more in 2026 than in 2023, and has nudged the coding format toward less pure LC hard and more practical implementation, but the medium-LC core remains.

The easiest FAANG to get a job at article includes a detailed comparison of conversion rates across the five companies in 2026, which shows Amazon's funnel is wider at the top and narrower in the middle than the other four. Candidates targeting Amazon should weight LP preparation heavily, treat the OA as a real filter, and not assume that strong coding alone carries the loop.

Apple: Massive Team Variance, Coding Still Universal

Apple's loop is uniquely decentralized among the five FAANG companies. The team owns hiring, the recruiter coordinates logistics, and the coding format varies more across Apple than across the other four combined. Some hardware-adjacent teams ask easy-to-medium LC and weight system thinking heavily. Some software services teams ask classic medium LC with standard follow-ups. Some specialized teams, particularly in low-level systems, OS, or compiler work, ask hard LC alongside language-specific deep questions. Some teams have shifted partially toward practical implementation rounds and weight architecture conversations more than puzzle solutions.

The constant across Apple is that coding shows up in every loop. There is no Apple team in 2026 that has eliminated coding interviews entirely. The variance is in difficulty distribution, in problem domain, and in how much weight non-coding rounds carry relative to coding. A candidate interviewing at Apple should ask the recruiter what the loop looks like for the specific team and prepare accordingly. Generic LC-medium preparation is the safe floor; depth in the team's domain is what differentiates above that floor.

Netflix: Coding Inside A Discussion-Heavy Panel

Netflix runs a five-round process: recruiter screen, hiring-manager call, coding round, system design round, and a culture-and-values deep dive. The coding round is the most concentrated LC-style portion, but the format is more conversational than the other four FAANG companies. The interviewer expects the candidate to work through 40 to 60 medium-to-hard LeetCode-style problems' worth of preparation depth, but the round itself is structured as a discussion about a problem rather than a pure timed solve. Graphs, dynamic programming, and concurrency questions surface frequently because they map well to Netflix's actual engineering surface.

The keeper-test culture and senior bar means Netflix applies a higher hiring threshold across every round, not just coding. The coding round is a filter, but the panel can fail a candidate for a strong coding performance paired with weak culture or system design rounds. LeetCode preparation is necessary but far from sufficient. The signal-to-noise ratio between technical strength and culture fit is more even at Netflix than at the other four FAANG companies.

Mini Q and A: Is Netflix's coding bar higher or lower than Meta's? Slightly lower in raw problem difficulty, slightly higher in conversational depth. Netflix expects fewer perfectly solved hard problems and more well-reasoned discussion of medium problems. The total signal asked is similar; the format that asks for it is different.

The Numbers Behind The Format

A consolidated table makes the variance across companies concrete. Frequency reflects how often a typical loop hits a pure LC-style round. Difficulty reflects the central tendency, not the extreme. Non-LC rounds reflect the rest of the loop a candidate should expect.

The takeaway is that the LeetCode round is present in every FAANG loop in 2026. The texture varies. The fact does not.

The AI-Resistant Question Selection Shift

The most underappreciated change in 2026 is not whether LC is asked but how the specific questions get chosen. Meta, Google, and Amazon have all shifted their internal question banks in the same direction: fewer pure pattern-match easies, more problems requiring a second insight after the first one, more problems where the optimal solution depends on a non-obvious observation, and more problems where the follow-up genuinely extends rather than restates the original. The shift is responsive to AI tools but is not a panic move; it is a recalibration of what the format measures.

The texture of a 2026 FAANG round looks like this. The interviewer presents a problem with a known LC analog. The candidate produces a first-level solution that works but is not optimal. The interviewer asks for the optimization, which requires recognizing that a different data structure changes complexity by an order of magnitude. If the candidate sees it, the interviewer extends the problem with a new constraint that breaks the optimization. The 2022 pattern of "solve a medium in 25 minutes and move on" fails against widely available AI tools. The 2026 pattern works because the second insight, the constraint extension, and the reasoning probe are exactly the things current models still struggle with under conversational pressure.

A Sample 2026-Style Problem With Follow-Ups

A concrete example shows how the format actually plays out in a round. The problem below is representative of what Meta or Google asks in 2026, with the follow-ups that distinguish a passing performance from a failing one.

# Problem: Given a list of intervals [start, end] representing meetings,
# return the minimum number of conference rooms required.
#
# Classic LeetCode 253 style. Familiar to anyone who has done the tagged list.

def min_meeting_rooms(intervals):
    if not intervals:
        return 0
    starts = sorted(i[0] for i in intervals)
    ends = sorted(i[1] for i in intervals)
    rooms = 0
    end_ptr = 0
    for start in starts:
        if start < ends[end_ptr]:
            rooms += 1
        else:
            end_ptr += 1
    return rooms

# Follow-up 1: now each meeting also has a priority [start, end, priority]
# and rooms have capacity limits. If a room is full, lower-priority meetings
# get bumped. Return the schedule with the minimum number of bumps.
#
# The candidate must shift to a heap-based simulation, track priorities,
# and reason about tie-breaking. The original two-pointer approach no
# longer works.

# Follow-up 2: meetings can be split across rooms if no single room is free
# for the full duration. Return the schedule that minimizes the number of
# splits while respecting room capacity.
#
# The candidate must now consider whether the problem reduces to a known
# scheduling variant, articulate the new objective function, and decide
# whether to model it as a flow problem or stick with simulation.

# Follow-up 3: the meeting list arrives as a stream, not a batch. New
# meetings can be added at any time, including with start times in the past
# relative to current wall-clock time. Maintain the current schedule.
#
# The candidate must now reason about online algorithms, the cost of
# rebalancing on each insert, and the trade-offs between recomputing from
# scratch and incremental updates.

A candidate solving only the original problem in 2026 gets a partial pass. The first follow-up separates strong candidates from weak ones. The second separates the strongest from the merely competent. The third usually does not surface unless time permits, but its presence in the interviewer's back pocket is why rounds remain interesting even when the base problem is well-known. For more on the pattern set, see dynamic programming patterns at FAANG.

Why The Format Survived The AI Wave

Three structural reasons explain why FAANG kept LeetCode-style rounds. The first is calibration: FAANG companies have decades of evaluation data tied to LC-style performance, and throwing out the format means throwing out the calibration. The second is signal density: a well-chosen medium with two strong follow-ups produces more signal per minute than almost any alternative format that has been tried. The third is the existence of an AI-aware question pool: internal teams at Meta and Google have demonstrated that they can write LC-style problems that current models do not solve confidently, giving the format a runway of several more years.

The companies that have moved away from LC are the ones whose engineering surface is genuinely different from FAANG's, not the ones who got scared of AI tools. Stripe's interviews focus on integration and debugging because Stripe's work looks like that. Anthropic's interviews look like research conversations because the engineering work is research-adjacent. Citadel's low-latency C++ rounds reflect the actual constraints of trading systems. The detailed treatment is in Stripe technical interview process and Anthropic technical interview process.

Alternative Formats: Where They Actually Live

A small but visible set of high-bar engineering companies have moved decisively away from LC-style rounds. The table below maps the major examples, with each company's format and the reasoning behind it.

Each of these companies has chosen a format that maps to its actual engineering. None of them eliminated coding evaluation entirely. The detailed breakdowns are in Stripe technical interview process, Anthropic technical interview process, Jane Street technical interview process, Linear technical interview process, Notion technical interview process, Palantir technical interview process, Shopify technical interview process, Pinterest technical interview process, Airbnb technical interview process, and Cloudflare technical interview process.

Mini Q and A: If a candidate prepares for FAANG, do they have to prepare separately for these alternative-format companies? Yes. The skills overlap significantly, but the rounds test different things. LC preparation builds algorithmic reflexes that translate to any technical interview, but a candidate walking into Stripe's debugging round with only LC reps will struggle with the format itself.

The AI-Enabled Round: What It Actually Tests

Meta's AI-enabled coding round and Google's pilot version share a structural pattern. The round gives the candidate access to an approved AI tool with explicit permission to use it. The problem is harder than a medium LeetCode question and typically requires 100 to 200 lines of code. The interviewer evaluates on four axes: how the candidate decomposes the problem into pieces the AI can help with, how they prompt for those pieces, how they verify the AI's output against requirements, and how they reason about the emerging solution.

The failure modes are predictable. Candidates who hand the entire problem to the model and accept the first output fail on verification: the model produces code that compiles but does not solve the specified problem. Candidates who refuse to use the AI tool out of habit fail on speed: the round is sized for AI assistance. Candidates who use the AI as an autocomplete on a problem they fully understand do well. The same evaluation philosophy threads through the best AI coding assistant for interviews and the best invisible AI tools for technical interviews. The implication for preparation is that 2026 candidates should rehearse the AI-enabled format separately from the traditional LC format. Both are present in real loops, and both deserve dedicated time.

How Many Hours, How Many Problems

The 150-to-250-problem range is the realistic floor for FAANG preparation in 2026, with the upper end weighted toward Meta and Google. The hour count varies by starting strength, but 200 to 400 focused hours is typical for a candidate moving from a non-FAANG background to a successful FAANG loop. The shape of the hours has shifted toward conversation rehearsal: a candidate who has solved 250 problems silently is not as prepared for the conversational depth of a Meta round as a candidate who has solved 150 problems out loud. The deeper treatment is in how many hours to prepare for FAANG interview.

Common Mistakes Candidates Make On FAANG LC Rounds

A recurring set of failure patterns shows up across candidate post-mortems from 2025 and 2026. Each is avoidable and each is independent of raw problem-solving ability.

1. Treating LC as dead and underpreparing. The most expensive mistake. The format is not dead, the bar is higher, and showing up with thin reps fails the round.
2. Optimizing for solution speed over reasoning depth. Speed mattered more in 2022. In 2026, an interviewer who sees a fast solution without articulated reasoning treats the round as less informative, not more.
3. Memorizing solutions without understanding patterns. The 2026 question pool is selected to defeat memorization. Recognition without explanation reads as recognition without understanding, which is the same outcome.
4. Skipping the clarifying questions. Every FAANG round expects clarifying questions. Diving into code without scoping the problem misses signal and often misreads the requirements.
5. Failing to surface edge cases unprompted. Strong candidates name edge cases before the interviewer asks. Weak candidates wait for the prompt and then react. The unprompted surfacing is half the round's signal.
6. Ignoring the AI-enabled format if interviewing at Meta or Google. Candidates who only prepare for the traditional format and get scheduled into the AI-enabled round walk in cold. The two formats need separate preparation.

The longer treatment of why otherwise strong candidates fail FAANG rounds is in why qualified candidates fail technical interviews and what interviewers look for in coding interviews. Both apply directly to the FAANG context because the underlying evaluation philosophy is shared.

The Pattern Set And The Senior Bar

The canonical LC patterns continue to dominate FAANG preparation in 2026: two pointers, sliding window, BFS, DFS, topological sort, union-find, monotonic stack and deque, prefix sum, binary search on the answer, segment trees, and the full DP family across subsets, intervals, trees, and graphs. The list has not shrunk. What has changed is the depth at which interviewers expect candidates to apply each pattern. Knowing that a problem is a sliding window question is no longer enough; the candidate must justify the window choice, explain the invariant, and adapt when the invariant breaks. The same approach is the through-line of machine learning engineer interview guide for the ML-specific variant.

Senior and staff loops at FAANG keep the LC rounds but reduce their weight relative to system design. A senior candidate at Meta or Google can expect one or two coding rounds with the same medium-difficulty profile, plus two or three system design rounds and an architecture round. Apple's senior loops vary by team. Amazon's senior loops add more LP weight and more Bar Raiser scrutiny. Netflix's senior bar is the most uniformly high across all rounds. The deeper system design treatment is in how to ace the system design interview.

Bottom Line

FAANG companies still ask LeetCode in 2026. The format has not died, has not been replaced, and has not even been meaningfully diminished. Meta runs two coding rounds. Google runs four to five. Amazon runs two to three plus a Bar Raiser. Apple runs varying coding rounds team-by-team. Netflix runs coding inside a discussion-heavy panel. The changes are about follow-up depth, AI-resistant question selection, and an emerging AI-enabled format at Meta and Google. The pattern set still matters. The hours still matter. The bar is higher, not different. Candidates who treated 2023's predictions as a license to skip LC preparation walked into 2026 loops underprepared. The candidates who treated the AI wave as a recalibration rather than an abolition, and who upgraded their preparation toward depth and conversation, walked in stronger.