How to Land a FAANG Internship in 3 Months: Summer Prep Guide

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If you're a CS student, chances are you've dreamt at some point of getting a chance to work with the FAANG/MAANG — Meta, Amazon, Apple, Netflix, or Google. I know preparing for these internships in 3 months might sound like a tall order, but trust me, it's possible with the right attitude and a clear plan. While your consistency and mindset depend on your personal goals, what I can help you with is creating a clear plan and helping you understand the process so you can prepare better—because the competition is brutal. Google alone gets millions of applications, with acceptance rates hovering around 2–3%.

So, I’ll start with how the hiring process works, then walk you through a 3-month plan along with all the resources that'll help you along the way. Let’s get started.

Understanding FAANG Internship Hiring

 
FAANG companies have multi-stage hiring processes designed to filter for top talent. Understanding this process should be the first step in your preparation journey. The process typically includes:

1. Application and Resume Screening: Applications usually open early (July–September) for the following summer and fill on a rolling basis. Your resume must pass Applicant Tracking Systems (ATS) and catch a recruiter’s eye. Keep it to one page, emphasize relevant coursework/projects, and use metrics (e.g., “improved performance by 30%”) and keywords from the job description. Many companies prioritize early applications since roles fill up quickly. Referrals from employees can help bypass initial filters—don’t ignore the power of networking.
2. Online Assessments: Most FAANGs start with coding tests on platforms like HackerRank or LeetCode. These assess your problem-solving and coding skills, typically focusing on data structures and algorithms. For data science roles, expect both coding (Python/SQL) and ML/statistics questions, along with case/problem-solving tasks.
3. Technical Interviews: Expect 2–4 rounds of live coding interviews, often via video call. You’ll solve DSA problems while explaining your thought process, along with time/space complexity analysis. Some companies may include system design questions, especially for more senior roles.
4. Behavioral Interviews: These focus on soft skills, cultural fit, and motivation (e.g., Amazon’s Leadership Principles or Meta’s STAR-format “Tell me about a time…” questions).
5. Team Matching (if applicable): Some FAANGs, like Amazon, match candidates with teams after interviews, which may involve additional conversations. Be ready with 2–3 solid project examples to talk about.

Month 1: Building a Strong Foundation

Week 1-2: Learn Core DSA and Coding Basics

FAANG interviews rely heavily on DSA. Getting comfortable with core structures early sets you up for more advanced topics. Start by mastering fundamental data structures: arrays, linked lists, stacks, queues, and hash maps. Understand how they work and when to use them.

Pick one programming language—Python is often preferred for its simplicity and is widely used in interviews. Start solving easy LeetCode problems like “Two Sum” or “Reverse Linked List.” Grind75 is a great (free) resource. Aim for 1–2 problems a day.

Week 3-4: Advanced DSA Part I and Starting Mock Interviews

Move on to more complex structures like trees, graphs, and sorting algorithms (e.g., binary search, quicksort). Learn traversals (e.g., in-order, pre-order) and basic graph algorithms like DFS. Start solving 2–3 medium LeetCode problems daily, focusing on patterns like two pointers or sliding window.

Start mock interviews. Pramp.com offers free peer-to-peer interviews. Do 1–2 mock sessions to get feedback on your explanations. I also recommend neetcode.io for clear explanations of LeetCode problems.

Month 2: Practice, Practice and Practice + Start Networking

Week 5-6: Advanced DSA Part II and Build Projects

Study dynamic programming, backtracking, and more advanced graph algorithms like BFS and Dijkstra’s algorithm. These often show up in hard interview problems. Solve 3–4 medium-to-hard problems daily and review optimized solutions.

Start working on 1–2 personal projects (like a full-stack web app or a Python game) to show initiative if you don’t have prior internship experience. Make sure your projects are clean, documented, and highlight your impact. AlgoExpert is a good paid platform for DP/graph visual learners.

Week 7-8: System Design, Behavioral Prep and Networking

If you're a junior or rising senior, system design is less likely to be tested. But if you're applying for more advanced intern roles, learn the basics (e.g., how to design a URL shortener). Google might ask simple design questions, while Meta often focuses on scale.

Build a one-page resume with job-related keywords like “Python,” “AWS,” or “full-stack.” Connect with 5–10 FAANG employees weekly on LinkedIn. Ask for informational chats or referrals—a simple message like “I applied for [X intern role] – would you be open to referring me?” works.

Start prepping for behavioral interviews. Write out 3–5 STAR-format stories focused on challenges, teamwork, leadership, and failures. Adapt your stories to company cultures (e.g., Amazon’s Leadership Principles, Google’s “8 Be’s,” or Meta’s Ownership value). Practice “Tell me about yourself” and “Why [Company]?”

Month 3: Mock Interviews and Applications

Week 9-10: Mock Interviews and Skill Refinement

This is crunch time. Simulate real interviews—do full loops (3–4 questions) with time limits. Interviewing.io offers mock interviews with FAANG engineers ($200–300 per session, scholarships available). Otherwise, use free options like Pramp or pair up with a friend.

Focus more on analysis than quantity now. Review your mistakes, polish your STAR answers, and practice delivering answers clearly and concisely.

Week 11-12: Apply and Final Interview Prep

Balance intense prep with rest. Get proper sleep, eat well, and avoid burnout. Use the Pomodoro method to stay on track.

Apply to all FAANG companies through their portals, and follow up with referrals wherever possible. Make sure your LinkedIn, GitHub, and portfolio are polished—interviewers may check.

If you're already interviewing somewhere, focus on nailing those. Follow up politely with recruiters if you haven’t heard back. And don’t forget to plan a small reward after interviews (a short trip or dinner out) to stay motivated.

Conclusion: Resources and Study Aids

• Coding Practice: LeetCode (especially Medium/Hard problems), HackerRank, InterviewBit. Books: Cracking the Coding Interview, Elements of Programming Interviews
• Online Courses:
  • Coursera: Algorithms Specialization (Princeton)
  • Udemy: Grokking the Coding Interview
  • MIT OpenCourseWare, Harvard’s CS50
  • Data science prep: Coursera (Andrew Ng’s ML), Kaggle courses, DataCamp tutorials, Kaggle competitions
• Mock Interview Platforms: Pramp, interviewing.io, CareerCup forums, DesignGurus
• Resume Help: Glassdoor blogs, InternStreet, LifeAt pages. Use Julia Evans’ “Brag Document” method to craft bullet points
• Networking Avenues: LinkedIn, alumni networks, campus career centers, engineering clubs, tech meetups, online communities like Slack/Discord groups
• Motivation & Time Management: Pomodoro timers (apps or browser extensions). Study schedules (Google Calendar reminders or a Trello board). Communities: Join or form a study group to stay accountable (as InternStreet suggests, pair programming or group study). Read blogs of other interns (like this one) for inspiration

Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook "Maximizing Productivity with ChatGPT". As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She's also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.