Meta Software Engineer Interview Guide

Comprehensive preparation for Meta SWE roles. Meta is one of the largest AI-focused employers globally, with a structured, well-documented interview process operating at planetary scale.

Interview Process Overview

Timeline: 3-6 weeks, 5-6 rounds

Round	Format	Duration	Focus
Online Assessment	CodeSignal (proctored)	90 min	Progressive coding problem (4 stages)
Recruiter Screen	Phone	30 min	Background, level calibration
Phone Screen	Coding (CoderPad)	45 min	2 LC medium problems
Onsite 1	Coding	45 min	Traditional DS&A
Onsite 2	AI-Enabled Coding	60 min	Coding with AI assistant (new 2025+)
Onsite 3	System Design	45 min	Large-scale distributed systems
Onsite 4	Behavioral	45 min	Meta core values alignment

New in 2025-2026: Meta introduced an AI-enabled coding round where candidates have access to an AI assistant in CoderPad. This tests your ability to leverage AI tools effectively, not just raw coding ability.

Compensation (E5 Senior, US)

Base: $190-220K
Equity: RSUs ~$250-500K/yr (vest quarterly after 1-year cliff)
Bonus: 15-25% of base
Total Comp: ~$450-700K (E5), ~$700K-1.2M+ (E6 Staff)
Meta refreshes equity aggressively for top performers

Key Themes

Speed and volume — Meta interviews are fast-paced. 2 coding problems in 45 minutes means ~20 min per problem. No time for hesitation.
Move fast — Meta’s culture values velocity. In behavioral, show you bias toward action and iterate quickly.
AI-first transformation — Meta is pivoting aggressively to AI (LLaMA, AI assistants, recommendation engines). AI knowledge is increasingly relevant.
Scale is the default — Everything at Meta serves billions of users. “How does this work at 3B users?” is always the lens.
Structured process — Meta’s interview is well-documented and consistent. This makes it one of the most “preparable” interviews.

Online Assessment (New Screening Step)

As of 2025, Meta uses CodeSignal with full video and microphone proctoring.

Format: 1 complex problem divided into 4 progressive stages that unlock sequentially
Duration: 90 minutes
Reported problems:
- In-memory database with key-value operations (SET, GET, DELETE, filtering, transactions)
- Cloud-based file storage service (upload, download, permissions, versioning)
- Task scheduling system with dependencies and priorities

This is very similar to Anthropic’s OA format. Production quality matters.

Coding Rounds (2 rounds)

Traditional Coding Round (45 min)

2 problems, medium difficulty, 20 minutes each + 5 min intro.

Meta draws heavily from these categories:

Category	Frequency	Classic Problems
Arrays/Strings	Very High	Two Sum, valid palindrome, move zeroes
Trees/BST	Very High	LCA, diameter, right side view, serialize
Graphs	High	Clone graph, course schedule, shortest path
Hash Maps	Very High	Group anagrams, two sum, subarray sum
BFS/DFS	Very High	Number of islands, word ladder, maze
Dynamic Programming	High	Longest palindromic substring, coin change
Binary Search	Medium	Search rotated array, find peak
Linked Lists	Medium	Merge sorted lists, reverse, cycle detection
Stacks/Queues	Medium	Valid parentheses, min stack
Intervals	Medium	Merge intervals, meeting rooms

Key Meta Coding Patterns

Two-Pass HashMap

def two_sum(nums: list[int], target: int) -> list[int]:
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

BFS with Level Tracking

from collections import deque

def right_side_view(root) -> list[int]:
    """Binary tree right side view -- last node at each level."""
    if not root:
        return []
    result = []
    queue = deque([root])
    while queue:
        level_size = len(queue)
        for i in range(level_size):
            node = queue.popleft()
            if i == level_size - 1:
                result.append(node.val)
            if node.left:
                queue.append(node.left)
            if node.right:
                queue.append(node.right)
    return result

Graph Clone (DFS with Visited Map)

def clone_graph(node):
    if not node:
        return None
    cloned = {}

    def dfs(n):
        if n in cloned:
            return cloned[n]
        copy = Node(n.val)
        cloned[n] = copy
        for neighbor in n.neighbors:
            copy.neighbors.append(dfs(neighbor))
        return copy

    return dfs(node)

Monotonic Stack

def daily_temperatures(temperatures: list[int]) -> list[int]:
    """Days until warmer temperature."""
    result = [0] * len(temperatures)
    stack = []  # indices of decreasing temperatures
    for i, temp in enumerate(temperatures):
        while stack and temperatures[stack[-1]] < temp:
            prev = stack.pop()
            result[prev] = i - prev
        stack.append(i)
    return result

AI-Enabled Coding Round (60 min, new)

This round gives you access to an AI coding assistant in the editor.

What they’re testing:

Can you effectively prompt the AI for help?
Do you understand the code the AI generates?
Can you debug AI-generated code?
Do you know when AI output is wrong?
Can you iterate and refine with AI assistance?

Strategy:

Use the AI for boilerplate, syntax reminders, and edge case generation
Always review and understand AI output before using it
Explain your reasoning to the interviewer — don’t just copy-paste AI output
The problems may be harder since you have AI help

System Design Round

Meta-Specific Focus Areas

Meta system design revolves around their products: News Feed, Messenger, Instagram, WhatsApp, and increasingly AI/ML systems.

Approach Framework

1. Clarify requirements (3-5 min)
   - Functional: What does the system do?
   - Non-functional: Scale, latency, consistency, availability
   - Constraints: Budget, team size, timeline

2. API Design (5 min)
   - Define key endpoints
   - Request/response schemas

3. High-Level Design (10 min)
   - Draw component diagram
   - Data flow

4. Data Model (5 min)
   - Schema design
   - Storage choices (SQL vs NoSQL vs graph)

5. Deep Dive (15-20 min)
   - Interviewer picks area to probe
   - Scale, reliability, performance

6. Trade-offs & Wrap-up (5 min)

Common System Design Questions

Design Facebook News Feed

[User Action] --> [Write Path]
                      |
               [Fan-out Service]
                      |
         +------------+------------+
         |            |            |
   [Feed Cache]  [Feed Cache]  [Feed Cache]
   (User A)      (User B)      (User C)
         |
   [Read Path] --> [Feed Ranking] --> [Response]

Key decisions:

Fan-out on write (push model): Pre-compute feeds for followers when a post is created
- Good for users with few followers
- Celebrity problem: Fan-out to 100M followers is expensive
Fan-out on read (pull model): Compute feed at read time
- Good for celebrities
- Higher read latency
Hybrid: Push for normal users, pull for celebrities

Feed ranking:

Features: Post age, engagement (likes, comments), relationship strength, content type
ML model: Deep learning ranker trained on engagement signals
Diversity: Ensure variety (don’t show 10 posts from same friend)

Design Instagram Stories

Write path: Upload video/image -> encode -> distribute to CDN
Read path: Fetch stories from followed users, sorted by recency and engagement
Ephemeral storage: Stories expire after 24 hours — TTL in storage layer
Ring UI: Client-side state management for viewed/unviewed

Design Facebook Messenger

Real-time delivery: WebSocket connections for online users, push notifications for offline
Message storage: Ordered by timestamp per conversation, sharded by conversation ID
Presence: Distributed presence service (who’s online now)
End-to-end encryption: Key exchange, message encryption/decryption
Group messaging: Fan-out within group, last-writer-wins for reactions

[Query: "Friends of friends who like hiking"]
    |
    v
[Graph Query Engine]
    |
    +-- [Graph Store (TAO)]  -- Entity and relationship storage
    |       |
    |       +-- [Cache Layer]  -- Heavily cached, read-heavy workload
    |
    +-- [Index Service]  -- Inverted index for attribute search

Storage: Adjacency list, sharded by entity ID
Caching: Aggressive caching (social graph is read-heavy, ~1000:1 read:write)
TAO: Meta’s graph store (Trillions of edges, billions of nodes)
Consistency: Eventual consistency for social graph, strong for auth/permissions

Design a Content Recommendation System (AI-Flavored)

[User Interaction Stream]
    |
    v
[Feature Service] --> [Candidate Retrieval] --> [Ranking Model]
                                                     |
                                              [Filtering & Safety]
                                                     |
                                              [Personalized Feed]

Two-tower model: User embedding + item embedding, nearest neighbor retrieval
Ranking: Multi-task learning (predict likes, comments, shares, time spent)
Exploration: Epsilon-greedy or Thompson sampling for new content
Safety: Content integrity classifier filters harmful/misleading content

Numbers to Know (Meta Scale)

Metric	Value
Daily active users	~3.2 billion (family of apps)
Posts per day	~1 billion
Photos uploaded per day	~350 million
Messages per day	~100 billion
Data stored	Exabytes
Cache hit rate (TAO)	>99.9%
News Feed reads/sec	Millions

Behavioral Round

Meta Core Values

Value	What They Assess
Move Fast	Bias toward action, iterative development
Be Bold	Take risks, think big
Focus on Long-Term Impact	Sustainable decisions, not quick fixes
Build Social Value	Care about user impact
Be Open	Transparent communication, share information

Common Questions

“Tell me about a time you had to move fast under uncertainty.”
“Describe a time you had to make a decision with incomplete data.”
“Tell me about a project that failed. What did you learn?”
“How do you handle disagreements with teammates?”
“Describe a time you had to balance speed with quality.”
“Tell me about a time you influenced a decision without authority.”

STAR Format Tips for Meta

Keep answers to 2-3 minutes (Meta interviewers often have many questions)
Quantify impact — Meta loves metrics
Show velocity — How fast did you move? Why?
Demonstrate learning — What would you do differently?

AI/ML at Meta

Key Technologies

LLaMA: Open-source LLM family (know architecture basics)
PyTorch: Meta’s ML framework (used everywhere)
FAISS: Vector similarity search (know for recommendation design)
Custom silicon: Meta is developing custom AI chips for inference

ML Infrastructure

Feature Store: Real-time features for recommendation models
Training infrastructure: Large-scale distributed training on custom GPU clusters
Model serving: Low-latency inference for ranking models across all products
A/B testing: Massive experimentation platform (thousands of concurrent experiments)

Preparation Tips

Leetcode Meta-tagged problems — Focus on the top 50 most-asked. Meta draws from a known pool.
Speed is critical — Practice solving mediums in 15-20 minutes. You need two in 45 minutes.
Write clean code fast — No pseudocode. Working code with good variable names.
Prepare for AI-enabled round — Practice using Copilot/Cursor effectively. Know when to trust and when to override.
System design at Meta scale — Always think in billions. “How many servers?” “What’s the storage cost?”
Read Meta engineering blog — engineering.fb.com. Focus on TAO, News Feed ranking, and ML infrastructure.
Behavioral is 25% of score — Don’t under-prepare. Have 6-8 strong stories ready.

Sources

Meta E5 Interview Guide - HelloInterview
Meta System Design Interview - IGotAnOffer
Meta Engineering Blog
Proven Meta Software Engineer Interview Guide - Prepfully
Senior Engineer’s Guide to Meta Interviews - interviewing.io
Glassdoor - Meta SWE Interview Questions
levels.fyi - Meta Compensation Data
r/cscareerquestions, Blind (community reports)