MAANG Companies: Full List, Hiring Process & How to Get In (2026)

MAANG stands for Meta, Amazon, Apple, Netflix and Google. It is the updated version of the older acronym FAANG, which used Facebook instead of Meta. The letters refer to the five large American technology companies widely treated as benchmarks for engineering compensation and hiring standards.

This guide covers what the acronym means, whether it still matters in 2026, how the hiring process actually works, what these companies pay in India, and a realistic preparation plan.

MAANG vs FAANG: what changed

FAANG was coined around 2013 and stood for Facebook, Amazon, Apple, Netflix and Google. When Facebook renamed itself Meta in 2021, the acronym drifted to MAANG.

Both acronyms are still used interchangeably. Neither is an official grouping, and the list has always been somewhat arbitrary.

Does MAANG still matter in 2026?

Less than it used to, and it is worth being honest about why.

The acronym excludes companies that now pay comparably or better, including Microsoft, Nvidia, OpenAI, Anthropic and several trading firms. It also excludes the fast growing Indian product companies and global capability centres where a large share of high paying engineering work actually sits.

What remains true is that these companies still run rigorous hiring processes, still pay in the top band, and still carry brand weight on a resume. What is no longer true is that they are the only destination worth targeting. Treating MAANG as the sole definition of success narrows your search unnecessarily.

What MAANG companies pay in India

These are 2026 estimates including base, bonus and equity, and vary by team, location and negotiation. Equity is a substantial portion at senior levels. For the broader market picture see decoding SDE salaries in India.

The hiring process, step by step

The process is remarkably similar across all five.

1. Resume screen. Often the hardest filter. Referrals meaningfully improve your odds here, more than any other single factor.

2. Online assessment. One to two algorithmic problems under time pressure, typically for campus and early career roles.

3. Phone or virtual technical screen. One or two data structures and algorithms problems, live coding, roughly 45 minutes.

4. Onsite loop. Usually four to six rounds covering:

• Two to three coding rounds focused on data structures and algorithms
• One system design round, which becomes central from mid level upward
• One behavioural round, which at Amazon is heavily weighted around its leadership principles

5. Hiring committee and team matching. A committee reviews feedback, then you are matched to a team.

The whole cycle typically takes four to eight weeks.

What they actually assess

Problem solving under observation. Not whether you memorised the solution, but whether you can reason out loud, clarify ambiguity, and recover when stuck.

Code quality. Clean, correct, tested code. Edge cases matter more than speed.

Communication. Explaining your approach before writing code is expected. Silent coding is penalised.

System design. From mid level onward this often carries more weight than the coding rounds.

Behavioural depth. Concrete stories with your specific contribution, not team generalities.

A realistic preparation plan

Months 1 to 3: fundamentals. One language deeply. Core data structures: arrays, strings, hashmaps, linked lists, stacks, queues, trees and graphs. Understand recursion properly, because it underpins most of the rest.

Months 3 to 6: patterns. Move from solving individual problems to recognising the underlying patterns: two pointers, sliding window, binary search variants, tree traversal, graph traversal and dynamic programming. Pattern recognition is what makes unseen problems tractable.

Months 5 to 7: system design. Load balancing, caching, database choices, sharding, queues and consistency tradeoffs. Practise explaining designs out loud.

Throughout: build things. Interviews are only one filter. Projects get you past the resume screen. See project ideas for engineering students.

Increasingly important in 2026: AI capability. These companies are all restructuring around AI, and candidates who can build with language models rather than only around them stand out. Practical AI and machine learning training is now a genuine differentiator in these loops rather than a side interest.

Improving your odds outside the interview

Get a referral. The single highest leverage action. Reach out to engineers with a specific, short message rather than a generic request.

Make your public work visible. A strong GitHub profile matters. See GitHub profiles every developer should follow for what good looks like.

Apply broadly, not narrowly. Indian product companies and global capability centres run similar processes with comparable pay and often faster progression. Preparing for MAANG prepares you for all of them.

Expect rejection and reapply. Most successful candidates were rejected at least once. Cooling off periods are typically six to twelve months.

Frequently asked questions

What is the full form of MAANG? MAANG stands for Meta, Amazon, Apple, Netflix and Google. It replaced the older FAANG acronym after Facebook became Meta.

What is the difference between MAANG and FAANG? Only the first letter. FAANG used Facebook, MAANG uses Meta after the 2021 rename. The companies are otherwise the same.

Is Microsoft part of MAANG? No, though this is a common criticism of the acronym. Microsoft pays comparably and is often grouped with these companies informally.

How long does it take to prepare for MAANG interviews? For most candidates with a programming base, six to nine months of consistent preparation covering DSA, system design and projects.

Do I need a degree from a top college to get into MAANG? No. College helps with initial screening, but referrals, projects and demonstrated ability matter more, and many engineers enter through non traditional paths.

Related reading: SDE full form | Understanding SDE levels | How to become an SDE in India