The single biggest 2026 shift in Meta interviewing is the AI-enabled coding round, where candidates spend one onsite block actively using an LLM coding assistant. The irony: Prepfully’s Meta hiring-committee coaches say plainly that Meta is NOT evaluating how you prompt AI. They are evaluating whether you stay the architect when the assistant produces something. Most candidates prep the wrong skill.
In this guide, we’ll cover the following 15 questions:
- Implement Basic Calculator II — and show me the O(1)-space optimization.
- Find the longest increasing subsequence — give me both O(n²) and O(n log n) approaches, then extend to count distinct subsequences.
- Return an array where each element is the product of all others — without using division.
- Walk me through this tree problem; now add this constraint at the end — what changes?
- Solve this maze-traversal problem using an LLM coding assistant. Walk me through your workflow.
- The assistant generated this code — walk me through whether you’d trust it, and what you’d do to verify.
- You have 45 minutes and an AI assistant. How do you allocate your time?
- Design a messaging app with read receipts and offline sync.
- How would you design Instagram’s home feed?
- Design an inference batching system for a single GPU.
- Why do you want to work at Meta — specifically?
- What’s the biggest mistake you’ve made at work?
- Tell me about a time you had to align requirements across teams.
- Walk me through the tradeoffs in Llama 3.1’s decision to use a decoder-only transformer at 405B scale rather than MoE.
- Design the data pipeline for training a foundation model at Meta-scale (15T tokens, 16K+ H100 GPUs).
How Meta’s 2024-2026 hiring shifted after the Year of Efficiency and Llama 3
Meta’s interview bar in 2026 is the product of two compounding shifts. The first is structural: the Year of Efficiency announced in March 2023 cut roughly 10,000 employees and closed 5,000 unfilled roles, and the cultural principles that came with it — “flatter is faster” and “leaner is better” — have not been retracted. Three more layoff waves followed in 2024-2025, including a 1,000-person Bay Area cut in October 2025 and the shutdown of Meta’s DEI programs in January 2025. Hiring managers now screen explicitly for cost-consciousness, project-cancellation judgment, and “garbage-collecting lower-priority work” — language drawn directly from Zuckerberg’s original efficiency memo.
The second shift is technical. Llama 3 launched in April 2024, followed by the Llama 3.1 405B release on July 23, 2024 — trained on 15 trillion tokens across 16,000 H100 GPUs. Llama 4’s multimodal release followed in 2025. AI is no longer a side bet at Meta; it is the org’s compensation outlier. As one r/csMajors thread documented in August 2025, Meta has offered $400K total comp to 22-year-old AI engineers — a number wildly out of band with the post-efficiency-era median.
What this means for the interview: the bar for general SWEs has gone up on judgment and down on patience, the bar for AI/ML engineers has gone up everywhere, and the 2026 AI-enabled coding round is the visible artifact of both shifts.
The 2026 Meta interview loop: 6 rounds from CodeSignal OA to team match
Meta’s 2026 interview process is six rounds plus team match. The full sequence: recruiter screen → CodeSignal online assessment → paired technical phone screen → culture-fit questionnaire → virtual onsite loop (4 interviews) → team match → offer. End-to-end timing is typically 3-4 weeks, though scheduling can stretch the loop and team-match can add 60-90 days before an offer materializes.
The CodeSignal Online Assessment is a 2025-era addition. Candidates take it under full video and microphone monitoring for the entire 90 minutes. It is a single complex problem in four progressive stages that unlock sequentially: Stage 1 covers basic get/set operations (testing fundamental correctness); Stage 2 adds constraints like TTL expiration; Stage 3 introduces point-in-time queries or versioning; Stage 4 demands deletion with concurrency handling. Recent OA prompts include designing an in-memory database or a cloud-based file storage service. Per HelloInterview’s E5 guide, most candidates do not complete all four stages within the time — and Meta knows this. Stage 3 is the realistic completion target.
The paired technical phone screen is 45 minutes in CoderPad — two medium LeetCode problems, no code execution. Interviewers explicitly look for narrated reasoning, clean-enough code that compiles in their head, and at least one tested corner case. Per Exponent’s 2026 guide: “minor syntax errors or small bugs won’t automatically fail you if your solution approach is sound.”
The culture-fit questionnaire, also new since 2025, is a 20-30 minute async multiple-choice screen. Exponent describes it as Meta’s “main way to get signal on soft skills/culture fit to filter out the obvious mismatches before committing to a full-day final round.” Treat it as a real gate, not a formality.
The virtual onsite is four interviews: one traditional coding round, one AI-enabled coding round, one system design or product architecture round (track-dependent), and one behavioral round. Each is 45 minutes. Behavioral carries unexpectedly high weight — HelloInterview’s E5 guide states bluntly that “your behavioral interview performance alone can often decide whether you’re hired as an E5 or potentially down-leveled to E4.”
Then comes team match, a structural risk every candidate should understand before starting the loop. Clearing the onsite does not guarantee an offer. As an ML Infra candidate documented in a November 2025 r/leetcode thread, candidates can clear the full loop and still be released after 60-90 days if no team picks them up. Amazon’s “holding room” pattern of keeping cleared candidates indefinitely does not apply at Meta. Plan accordingly.
Coding round questions and how Meta scores them
Meta’s two onsite coding rounds plus the phone screen share a specific philosophy. Interviewers are officially instructed not to ask pure dynamic programming — 45 minutes is insufficient for novel DP, and they want to avoid rewarding rote memorization. In practice, they ask LeetCode-tagged medium-to-hard problems heavy on arrays, strings, linked lists, binary trees, and graphs. The Meta-tagged top 100 on LeetCode is the realistic study target.
Implement Basic Calculator II — and show me the O(1)-space optimization.
Concept: Stack-based parsing, constant-space arithmetic evaluation | Difficulty: Medium (with senior follow-up) | Stage: Phone screen / onsite coding
Direct answer: Basic Calculator II asks you to evaluate a string expression like "3+2*2" with +, -, *, / and integers. The standard O(n) approach uses a stack: push numbers, apply * and / immediately when their operator was the last one seen, defer + and – until you sum the stack at the end. The senior-track follow-up asks for the O(1) space variant: track a running total, a current value, and the last operator as you iterate the string — no stack required. Multiply or divide the current value when the last operator was * or /, otherwise add or subtract into the running total. This trades one mental model for another and is the differentiator under time pressure.
What they’re really probing: Whether you recognize that most parsing problems with a single precedence level can collapse to constant space, and whether you can produce that insight without the interviewer telling you the trick exists.
This question recurs heavily in Meta phone screens. User u/ABGinTech, a self-identified cleared-Meta candidate, names it directly when describing Meta’s pattern: “very commonly people suggest the O(N) stack solution but there is a clever O(1) space optimization.” Failing to find the optimization under time pressure is a soft-negative signal at E4 and above; missing it entirely is a hire-no at E5.
Find the longest increasing subsequence — give me both O(n²) and O(n log n) approaches, then extend to count distinct subsequences.
Concept: Recognized-pattern DP under the no-novel-DP rule | Difficulty: Medium | Stage: Phone screen / onsite coding
Direct answer: The O(n²) approach uses a tabulation array where dp[i] stores the length of the longest increasing subsequence ending at index i; for each i, scan all j < i and take the max. The O(n log n) approach uses patience sorting: maintain a “tails” array and binary-search for the position of each new element. The extension — counting distinct increasing subsequences — requires a parallel count array tracking how many subsequences produce each ending length, which Meta interviewers flag as the senior-track filter.
What they’re really probing: Pattern recognition. This is recognized-pattern DP (the LeetCode 300 family), which is explicitly fair game even under Meta’s no-novel-DP guidance. The signal is whether you immediately reach for the binary-search variant or hand-roll the O(n²) and stop there.
A May 13, 2026 anonymous candidate posting an accepted-offer interview report on Glassdoor describes being asked exactly this question, including the count-distinct extension — confirming the pattern is current. The interviewer specifically expected both complexity classes and the follow-up extension.
Return an array where each element is the product of all others — without using division.
Concept: Prefix/suffix pass, space optimization | Difficulty: Medium | Stage: Phone screen / onsite coding
Direct answer: Given nums with n > 1, return output where output[i] is the product of all elements except nums[i] — no division allowed. The optimal approach is two passes: first pass fills output[i] with the prefix product (everything to the left of i); second pass walks right-to-left maintaining a running suffix product and multiplies it into output[i]. This is O(n) time and O(1) extra space (the output array doesn’t count). A naive O(n²) double loop is hire-no.
What they’re really probing: Whether you recognize that the no-division constraint is a hint, not a hurdle — and whether you discuss the zero-handling case explicitly (one zero: only that index is nonzero; two zeros: all output entries are zero).
IGotAnOffer’s 2026 top-five Meta questions, compiled with input from Damien P. (ex-Meta product manager) and Pranav P., lists this verbatim. The reason it stays in rotation: it tests prefix-suffix thinking, edge-case discipline, and willingness to articulate constraints — all in 15 minutes of code.
Walk me through this tree problem; now add this constraint at the end — what changes?
Concept: Mid-problem requirement change, adaptive design | Difficulty: Medium-Hard | Stage: Onsite coding (round 2)
Direct answer: The Meta “twist at the end” pattern starts with a recognizable tree problem — invert a binary tree, find lowest common ancestor, validate a BST — and adds a requirement change in the last 10 minutes. The shift might add a parent pointer, ask for iterative-only, demand thread-safety, or require returning all paths instead of just one. Engineers who locked in a tightly optimized solution have to rewrite. Engineers who kept their solution decomposed — separate traversal from accumulation, separate state from logic — adapt the existing function and pass.
What they’re really probing: Whether your original design generalizes. This is the round where over-engineered “elegant” solutions fail and modular, somewhat-verbose solutions succeed.
The pattern is documented in the $295K Meta offer thread on r/leetcode from May 2025, where the named candidate describes the second coding round as “a tree problem that required a twist at the end, and I barely got there in time.” It is not a single canonical question — it is a routing pattern Meta interviewers apply across the tree-problem space.
The AI-enabled coding round: what Meta actually scores (not what candidates think)
The AI-enabled coding round is the most misunderstood round in Meta’s 2026 loop. Candidates spend hours practicing prompt-engineering tricks; none of that is what the interviewer grades. Prepfully’s Meta coaches, who sit on hiring committees, state the rubric explicitly: “Meta is NOT evaluating how you prompt AI.” The round assesses whether you maintain engineering rigor while moving faster — whether you stay the architect when the assistant produces something — not whether you are a prompt expert.
Five evaluation signals matter and three anti-signals sink the round. Signals: ownership of the final solution; verification through testing and tracing; critique of generated code line by line; debugging when the assistant is wrong; communication of intent and tradeoffs throughout. Anti-signals: copy-pasting LLM output without testing; over-trusting structurally wrong code; treating the assistant as an oracle.
Solve this maze-traversal problem using an LLM coding assistant. Walk me through your workflow.
Concept: AI-tool collaboration under signal, not prompting technique | Difficulty: Medium (problem) + ambiguous (signal) | Stage: AI-enabled coding round (onsite)
Direct answer: The interviewer poses a maze-traversal problem — typically BFS through a 2D grid with obstacles — and asks you to use a Meta-approved LLM model. Open the assistant, type your problem framing in plain English, and request a first draft. Read the generated code line by line aloud, flag any line you don’t fully understand, and either ask the assistant for clarification or rewrite that line yourself. Test against the provided examples. When new obstacles or constraints are added mid-round, decide whether to extend the existing solution or ask for a fresh draft — and narrate that decision.
What they’re really probing: Whether your workflow stays recognizably yours. The interviewer wants to see the candidate stay in the driver’s seat, not get pulled into the LLM’s narrative momentum.
Per Exponent’s 2026 Meta guide, written with input from real Meta candidates: “You will also be tested on how well you assess model output, as they will want to see you checking the AI coding assistant’s work rather than just copy-pasting its solution into your own.” The maze problem itself is medium difficulty — the depth lives in the workflow narration.
The assistant generated this code — walk me through whether you’d trust it, and what you’d do to verify.
Concept: Code review against AI output under time pressure | Difficulty: Senior | Stage: AI-enabled coding round follow-up
Direct answer: The interviewer hands you LLM-generated code (sometimes deliberately containing a subtle bug — off-by-one, missed edge case, wrong asymptotic class) and asks for your verification process. Walk through it: trace one example input line by line; identify which lines depend on external assumptions; check edge cases (empty input, single element, max input, negative values, duplicates); look for the asymptotic complexity claim and verify it against the code. If the code has a bug, name it; if it doesn’t, explain why you’re confident it doesn’t.
What they’re really probing: Whether you read generated code with the same skepticism you’d apply to a colleague’s PR — or whether you scan it and approve.
The signal Meta interviewers explicitly watch for, per Prepfully’s rubric: “Ability to verify and validate outputs instead of trusting them blindly.” Specific behaviors that score high: tracing edge cases through generated code, asking the LLM clarifying questions when output is ambiguous, rejecting LLM output when it is structurally wrong rather than just syntactically off.
You have 45 minutes and an AI assistant. How do you allocate your time?
Concept: Workflow time-allocation under AI augmentation | Difficulty: Mid-senior | Stage: AI-enabled coding round, opening
Direct answer: A senior-track answer breaks the 45 minutes into understanding (5 min), planning (5 min), iterative implementation with the assistant (25 min), and verification/testing (10 min). The understanding phase is where you re-read the problem and articulate constraints in your own words. The planning phase is where you sketch the solution shape on paper or in comments before any AI prompt. The iterative phase is where you use the assistant for tactical generation while keeping the structural choices yours. The verification phase is non-negotiable — it is the single biggest discriminator between strong and weak candidates in this round.
What they’re really probing: Whether you treat the assistant as a multiplier on your existing engineering process or as a substitute for it. The anti-signal answer: “I’ll just ask the AI for a solution and refine.”
The time budget itself is not the deliverable — the explanation is. Per the same Prepfully rubric: interviewers want to see “Maintaining engineering rigor while moving faster” and “Signal that you can work effectively in a modern Meta codebase where AI tooling exists.” Both are workflow signals, not output signals.
System design and product architecture questions
System design at Meta forks by track. Software Engineer, Infrastructure roles get the system design interview (distributed systems, partitioning, CAP tradeoffs). Software Engineer, Product roles get the product architecture interview (API design, client-server interactions, product evolution). Both run 45 minutes and scale with level: senior+ candidates own the whole distributed system, below-senior candidates focus on API details. Meta’s mobile product tracks are evaluated separately.
Design a messaging app with read receipts and offline sync.
Concept: Distributed systems, consistency-availability tradeoffs | Difficulty: Mid-Senior (E4/E5) | Stage: System design (onsite)
Direct answer: Sketch the four core components: clients (mobile + web), a messaging gateway, a message store (with append-only log), and a notification service. Discuss the consistency-availability tradeoff explicitly — read receipts demand eventual consistency, offline sync demands client-side queueing with conflict resolution. Senior-track follow-ups dive into message ordering across regions (vector clocks or causal ordering), sync-after-offline reconciliation (last-write-wins vs CRDT), and end-to-end encryption interplay. Stay high-level for the first 25 minutes; only dive into one component when asked.
What they’re really probing: Whether you can name the hard tradeoff up front. Engineers who jump into a database schema before discussing CAP fail; engineers who frame “this is fundamentally a consistency problem — here’s the spectrum” pass.
The named candidate behind the $295K E4 Meta offer (May 2025 r/leetcode) describes their system design round as exactly this question: “I walked through designing a messaging app with read receipts and offline sync, leaned hard on consistency vs availability tradeoffs, and tried to keep it high level without overengineering anything.” The deliberate restraint is the senior-track signal.
How would you design Instagram’s home feed?
Concept: Feed-ranking, media CDN, timeline-vs-feed tradeoffs | Difficulty: Mid-Senior | Stage: Product architecture
Direct answer: Name the components: post-storage service, media CDN for images and video, feed-generation service, ranking model serving, cache layer, and client-side personalization. The E4 floor: enumerate the components and describe one end-to-end request. The E5 signal: the pull-vs-push hybrid tradeoff — pre-generate feeds for the top X% of users with the most followers, on-demand for the long tail. The E6 signal: ranking-model serving infrastructure, A/B testing the ranking changes safely, latency SLOs at the 99th percentile, and the eventual-consistency-friendly nature of social feeds.
What they’re really probing: Whether you treat ranking as a first-class system or as a black box. Skipping the ranking model entirely is the strongest negative signal — Meta’s product is the ranking model.
This question recurs across IGotAnOffer’s 2026 top-five, The Interview Guys’ top-ten product-track list, and Glassdoor reports going back two years. The 2024-2026 variants substitute Threads (where the federated-server question becomes core), Ray-Ban Meta glasses (where the on-device-vs-cloud question becomes core), or Reels (where the recommendation-system question becomes core). The product changes; the architecture rubric does not.
Design an inference batching system for a single GPU.
Concept: ML systems, GPU economics, latency-throughput tradeoff | Difficulty: Senior (E5+ ML Infra) | Stage: System design (ML Infra track)
Direct answer: The constraint: a single GPU, up to 100 inputs per batch, synchronous user-facing latency. Sketch the request queue, the dynamic batching window, the batch dispatcher, the inference engine, and the response router. Name the concrete tradeoffs: dynamic batching window vs P99 latency (longer window = better GPU utilization but worse tail latency); padding cost for variable-length sequences (sort by length within a window, or use bucketing); KV-cache reuse across requests sharing a prefix; fairness across users. Senior-track candidates also propose a load-test rubric and SLO definition.
What they’re really probing: ML systems literacy. This is a question with a known correct answer shape — Meta’s own production batching systems use exactly these patterns — and the signal is whether the candidate knows it.
The question appears in Exponent’s 2026 ML question bank and aligns directly with the architecture described in Meta’s March 2024 engineering blog on its 24K GPU clusters. Senior AI engineering candidates targeting Meta’s GenAI or FAIR teams should expect this exact problem shape.
Behavioral questions in the efficiency era (cost-consciousness as a real probe)
Meta’s behavioral round is shorter than Amazon’s or Apple’s — 30-45 minutes, fewer questions, lower follow-up density. Exponent characterizes it as “more like Microsoft, where behavioral screens are more of a checkbox.” That framing understates what changed in 2024-2026: the round is still short, but the questions now probe cost-consciousness and project-cancellation judgment in ways they did not before 2023. Generic “tell me about a time” stories miss the new bar. And per HelloInterview, behavioral performance alone can drop an E5 candidate to E4.
Why do you want to work at Meta — specifically?
Concept: Mission alignment with named product depth | Difficulty: All levels | Stage: Recruiter screen AND hiring manager
Direct answer: Name specific Meta products you have personally used and one technical or strategic position Meta has taken that you find compelling. Personalize — connect a product to your own life (WhatsApp for staying in touch with family abroad; Threads’ federated approach as a contrast to the centralized social model; Ray-Ban Meta glasses as the latest bet on ambient computing). Cite Meta’s open-source posture if you’re applying to AI roles — Llama 3.1 released as the first frontier-level open-source 405B model is a specific, defensible reason. Aim for two to three concrete reasons; one is thin, four or more reads as preparation theater.
What they’re really probing: Whether your reasons swap to any FAANG company by replacing the name. Per IGotAnOffer’s Damien P., a former Meta product manager: “If your answer can easily be used for other companies by just swapping the company names, it’s still too broad and you need to keep working.”
The question opens nearly every recruiter screen and recurs in the hiring manager round. The bar is real: candidates who lead with “impact at scale” or “great engineering culture” fail this question, because both reasons are true of every FAANG company. Answers that name a specific Meta product, paper, blog post, or strategic position pass.
What’s the biggest mistake you’ve made at work?
Concept: Self-awareness, cost-of-mistake judgment in the efficiency era | Difficulty: Mid-Senior | Stage: Behavioral
Direct answer: Pick a real mistake with a quantifiable cost — engineering hours wasted, dollars spent, scope missed, users affected. Describe what you stopped doing afterward, not just what you learned. The 2026 anti-pattern: “I once shipped a bug that broke production for two hours, and now I always write tests.” That answer was fine in 2020. The senior-track 2026 answer: “I once kept a low-ROI project alive for six months because no one had explicitly killed it; the cost was four engineers’ time and a missed Q3 goal. I now propose a kill criterion at every project kickoff.” That answer mirrors Meta’s “garbage-collect unnecessary processes” language from the Year-of-Efficiency memo and signals you have internalized the post-2023 culture.
What they’re really probing: Whether you can audit your own decision-making for opportunity cost. Mistakes that show technical failure are floor-level; mistakes that show poor prioritization judgment are senior-track gold.
The pattern is reported across Glassdoor’s 2,682-interview May 2026 index and confirmed by recent web summaries of Meta behavioral rounds. The question itself is not new; what’s new is the rubric for what counts as a strong answer in the efficiency era.
Tell me about a time you had to align requirements across teams.
Concept: Cross-functional collaboration, influence without authority | Difficulty: Senior (E5+) | Stage: Behavioral
Direct answer: Use the SPSIL framework that IGotAnOffer’s Meta-specific coaches recommend: Situation (minimum context), Problem (what was misaligned), Solution (what you did, focused on your contribution), Impact (quantified outcome), Lessons (what you’d repeat or change). The E5 signal is a story where you quantify the impact — “I reduced rework hours by 40% by getting both teams in one meeting with a shared roadmap”; the E6 signal is a story where you influenced without authority across an org boundary — “I had no headcount over the data infra team, but I built a shared metrics dashboard that made it obvious why their schema choice was costing my team a week of rework per sprint.”
What they’re really probing: Real conflict with named stakes, not sanitized “we agreed to disagree” stories. Interviewers have rejected candidates explicitly on insufficient depth here, per multiple Glassdoor reports.
The named-name anti-pattern matters: do not invent recruiters or engineers. “Sarah from product said…” with no verifiable context is a soft-negative signal per recurring Glassdoor feedback. Use real first names of real colleagues, or omit names entirely.
The Meta E3-E7 scoring matrix: same questions, different rubrics
The same coding question, the same system design prompt, the same behavioral story can land a candidate at E3, E4, E5, E6, or E7 — or sink them entirely — depending on what they emphasize. Prepfully’s 2026 guide, informed by named Senior Engineering Managers, Staff Engineers, and a Senior Staff Engineer currently sitting on Meta hiring committees, publishes the level rubric. What that source does not do — and what no top-ten SERP result does — is map how a single question gets scored differently across all five levels. The table below does that.
| Level | Coding signal | System design signal | AI-enabled coding signal | Behavioral signal |
|---|---|---|---|---|
| E3 (SWE, new grad) | Correct DS/A on medium problem; asks clarifying questions; narrates approach | Names components; describes one happy-path request | Uses assistant to check syntax and look up unfamiliar APIs; doesn’t over-rely | Strong CS fundamentals; learning velocity; clear communication |
| E4 (SWE) | Optimal complexity on medium; handles edge cases; iterates from rough to clean | End-to-end design with named tradeoffs; one bottleneck identified | Verifies generated code with test cases; reads it line by line | Ownership of outcomes; sensible tradeoffs; comfortable with some ambiguity |
| E5 (Senior SWE) | Recognizes the pattern (e.g. patience sort); discusses two solutions and picks one; handles the “twist” | Names CAP tradeoff up front; depth on at least one component; SLO awareness | Critiques generated code structurally; rejects wrong output; communicates intent throughout | System-level thinking; quantified impact stories; influence on peers; cost-consciousness |
| E6 (Staff SWE) | Anticipates the follow-up before it’s asked; proposes alternate solution if first is suboptimal | Multi-system architecture; anticipates failure modes; observability and ops | Treats assistant as a code-review opportunity; flags subtle bugs in generated code | Influence without authority; cross-org judgment; project-cancellation stories |
| E7 (Senior Staff / Principal) | Frames the problem space, not just the question; technical direction; mentorship signals | Org-wide systems; foundational technical bets; multi-quarter scope | Discusses team-wide adoption of AI tooling; production-grade workflow integration | Setting technical direction; sustained impact at scale; managing complexity across systems |
Two structural facts about the matrix matter more than any single cell. First, down-leveling is real and common: HelloInterview’s E5 guide states bluntly that “your behavioral interview performance alone can often decide whether you’re hired as an E5 or potentially down-leveled to E4.” The behavioral round is not a checkbox at senior levels. Second, team match is a third gate beyond level calibration. Candidates can clear the loop at E5 and still be released after 60-90 days if no team selects them — the structural risk we covered earlier and that senior software engineer interview prep on most sites does not warn about.
ML / FAIR / GenAI track: questions tied to Meta’s published research
Meta’s ML and AI tracks — FAIR (Fundamental AI Research), GenAI product, ML Infrastructure, and Reality Labs ML — are interviewed against a partly separate rubric from general SWE. Coding rounds remain (often more lenient), but system design tilts heavily toward ML systems and research interviews probe whether candidates have read Meta’s specific published research. Compensation reflects the bar shift: r/csMajors threads in August 2025 documented $400K total comp offers to 22-year-old AI engineers. Two questions illustrate the rubric.
Walk me through the tradeoffs in Llama 3.1’s decision to use a decoder-only transformer at 405B scale rather than MoE.
Concept: Frontier-model architecture design, training-stability vs efficiency tradeoff | Difficulty: Senior+ (E5/E6 ML) | Stage: ML system design / research interview
Direct answer: Llama 3.1’s 405B model is a standard decoder-only transformer, not a mixture-of-experts architecture, despite MoE’s theoretical training-efficiency advantage at this scale. Meta’s official release post explicitly names the reason: “We opted for a standard decoder-only transformer model architecture with minor adaptations rather than a mixture-of-experts model to maximize training stability.” At 16,000 H100 GPUs across 15 trillion training tokens, every additional architectural complexity multiplies the failure surface. The team also adopted an iterative post-training procedure — supervised fine-tuning plus direct preference optimization (DPO) in each round — which itself benefits from a stable underlying architecture. The 8B and 70B variants share the same architectural choice.
What they’re really probing: Whether you’ve actually read the Llama 3.1 paper or just heard about it. Candidates who answer “MoE is more efficient” miss the question entirely — the question is about the stability constraint, not the efficiency frontier.
The architecture choice is unusual enough that r/MachineLearning threads have debated it publicly, and the interviewer is testing whether the candidate engages with Meta’s actual reasoning or substitutes a generic “decoder-only is simpler” handwave. Senior-track candidates also name the synthetic-data-generation use case for 405B (improving smaller 8B and 70B models through distillation) as a downstream consequence of the architectural choice.
Design the data pipeline for training a foundation model at Meta-scale (15T tokens, 16K+ H100 GPUs).
Concept: ML infrastructure, training data curation, GPU orchestration at scale | Difficulty: Senior+ (E5/E6 ML Infra) | Stage: System design (ML Infra track)
Direct answer: Sketch the seven components: data ingestion from multiple sources (web crawls, code, books, multilingual corpora); deduplication and quality filtering (fuzzy hash for near-duplicates, classifier-based quality scores); tokenization throughput matched to GPU appetite; pretraining vs post-training data separation (different curation pipelines and quality bars); distributed sharded training across 16K H100s with tensor + pipeline parallelism; checkpointing at scale (frequent enough to recover from GPU failures, infrequent enough to not bottleneck training); recovery and resharding when GPUs fail mid-run. Senior-track candidates name the iterative post-training cycle where Llama 405B generates synthetic data for the 8B and 70B models — a real Meta production pattern.
What they’re really probing: Production ML infrastructure literacy at Meta’s specific scale. The answer should anchor on Meta’s published architecture, not a generic “DDP across GPUs” sketch.
This is the ML Infra equivalent of “design Instagram.” It is not a blank-canvas design problem — Meta’s March 2024 engineering blog on 24K GPU clusters for LLM training describes the actual architecture publicly. Candidates who have read it pass; candidates who reason from first principles without that grounding produce designs that miss real constraints (checkpointing cost, intra-batch ordering across pipeline stages, failure detection time budgets).
Questions to ask your Meta interviewer (the senior-signal version)
The questions you ask in the closing five minutes signal your seniority more reliably than most candidates realize. Generic “what’s the culture like?” questions are floor-level; specific operational questions are senior-track. Six questions worth bringing to your Meta loop:
- “How did your team decide which lower-priority projects to garbage-collect in the past year?” This signals you have internalized the post-2023 efficiency culture and treat project cancellation as a real engineering responsibility, not an HR euphemism.
- “How is the team integrating LLM coding assistants into the daily workflow now that the AI-enabled coding round is the bar?” Asks the interviewer to describe their actual workflow — useful information for you, signals you understand the round wasn’t a one-off.
- “What does the first-30-days ramp look like given the post-2023 flattening?” Signals you know about the efficiency-era manager-IC ratio shift and want to understand the expected pace.
- “How do you decide whether to invest in a custom internal tool vs adopting an external library, given the cost-consciousness lens?” A real tradeoff senior engineers face; asking it shows you understand the constraint.
- (ML track only) “How does FAIR coordinate with the GenAI product org on Llama post-training, and where does that boundary cause friction?” Anchors on the public org structure and probes for honest internal politics.
- “What signal would you have wanted me to give in the system design round that I didn’t?” A high-trust question. Asks the interviewer to act as a coach for two minutes and surfaces useful feedback whether or not you get the offer.
A 7-day prep sequence using Meta’s own published research
A focused week of prep is more effective than three unfocused months. The sequence below assumes you have a Meta loop scheduled in 7-14 days and that you have working DSA fundamentals (you can solve a LeetCode medium in 30 minutes in a quiet room). If you cannot do that yet, push the loop and come back to this prep when the foundation is in place.
- Day 1 — Read Meta’s own posture. Read the Meta SWE prep portal pages your recruiter sent (the hiring portal contains role-specific material). Read the Year of Efficiency memo from March 2023 in full — it is the cultural source document for behavioral round expectations.
- Day 2 — Read the ML / FAIR research. Read the Llama 3.1 release post (the architecture choice section is exam material for ML candidates). Read the 24K GPU cluster engineering post from March 2024. ML candidates: skim the Llama 3.1 paper itself; SWE candidates: the blog posts suffice.
- Day 3 — Practice 10 Meta-tagged LeetCode mediums. Focus on arrays, strings, binary trees, graphs. Use the timer: 20 minutes per problem. Aim for 1.5 solutions out of every two — that is the calibrated bar per the 1267-upvote “Meta pace is insane” thread and matches the cleared-Meta posts in the same thread.
- Day 4 — Practice the CodeSignal 4-stage pattern. Pick an in-memory database problem (LeetCode “Design HashMap” + extensions, or a custom problem). Write Stage 1 in 20 minutes, Stage 2 (TTL) in 20 minutes, Stage 3 (versioning) in 25 minutes. Use a real timer and stop when each stage’s clock expires — the OA cuts you off mid-thought.
- Day 5 — Mock two system design problems. Run a 45-minute mock on “design a messaging app with read receipts and offline sync” and another on “design Instagram’s home feed.” Record yourself if no partner is available; review the recording the next morning for over-engineering tells and missed CAP tradeoffs.
- Day 6 — Practice AI-enabled coding for 90 minutes. Open Cursor, Claude, or GitHub Copilot. Pick a medium LeetCode problem you have not seen. Solve it with the assistant active; narrate your verification process aloud as you go. Stop after 45 minutes. Replay the audio: count how many times you trusted output without testing. That count is your starting deficit.
- Day 7 — Write six behavioral stories. Use SPSIL: Situation, Problem, Solution, Impact, Lessons. One story each on: a project you killed early; a low-ROI project you kept alive too long; a cross-team alignment you led; a real mistake with a quantified cost; a time you disagreed with your manager; the most ambiguous problem you’ve owned. Each story 90 seconds spoken aloud. Don’t memorize the words — memorize the structure.
The deepest takeaway across all of Meta’s 2024-2026 interview rounds is that the bar has bifurcated. For general engineers, the floor has risen on judgment and cost-consciousness, and the patience for slow-ramp hires has compressed to roughly 30 days. For ML and AI engineers, the bar has lifted everywhere and the compensation has lifted with it. The AI-enabled coding round is the visible artifact of both shifts: it signals that Meta expects working engineers — at every level — to integrate LLM tooling into their daily workflow without losing ownership of the code that ships. Walk into the loop having internalized that, and you will not be surprised by what the rounds actually test.
For broader senior-level interview preparation across companies, see our guide to senior software engineer interview questions. For the AI-engineering track specifically, see AI engineer interview questions. For Meta’s React-lineage coding round, see ReactJS coding interview questions.