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Indian-Origin Techie Lands Rs 3.5 Crore Job at Meta

TL;DR

• Manoj Tumu, 23, landed a coveted machine learning engineer role at Meta (Facebook’s parent company) with a total annual compensation of about Rs 3.5 crore ($400,000).
• He transitioned from Amazon to Meta, driven by a desire for more challenging work.
• He credits practical experience, strategic resume-building, and focused interview preparation for his rapid rise in the tech industry.

Introduction: Young Achiever Makes Headlines at Meta

At just 23 years of age, Indian-origin software engineer Manoj Tumu stunned the technology world by joining Meta (formerly Facebook) as a machine learning software engineer on a staggering annual compensation package exceeding Rs 3.5 crore. Manoj’s career trajectory—leapfrogging from Amazon to one of the world’s most influential tech companies—has become an inspirational case study for thousands of tech aspirants across the globe.

In this post, we break down how Manoj achieved this milestone, the lessons aspirants can learn, and actionable tips to make your own mark in big tech, especially in the fields of artificial intelligence and machine learning.

Who is Manoj Tumu? The Journey from Amazon to Meta

Manoj Tumu, an Indian-American techie, began his journey in the United States, pursuing higher education and internships that paved the way for a lucrative tech career. In 2022, he embarked on his master’s degree, supplementing his formal education with real-world programming and machine learning projects. His break came with Amazon, where he rapidly adapted to the rigor and culture of big tech.

By June 2025, Manoj left Amazon to accept an offer from Meta. As he detailed in a Business Insider article, the opportunity was irresistibly exciting and immediately felt like the right move. With a compensation exceeding $400,000 per year, Manoj stepped into the Ad Research Team at Meta, fast-tracking his rise in the AI and machine learning space.

Why Did Manoj Move? The Lure of Meta’s Projects

“Though I had learned a lot at Amazon, I just thought there was more interesting work going on at Meta,” Manoj recalled. Amazon provided foundational experience and exposure, but Meta’s culture of innovation, especially in deep learning and scalable AI, drew him in.

He recognized that the tech landscape is shifting rapidly—from classical, human-designed data representations toward sophisticated deep learning architectures that leverage artificial neural networks to automatically extract insights from massive datasets. Meta, being at the forefront, offered Manoj the ideal playground to contribute to transformative projects.

Key Lessons from Manoj’s Career Leap

Manoj’s story provides a roadmap for other aspiring tech professionals, especially those from India and the Indian diaspora. Below, we break down the main takeaways:

1. Internships Matter—Pay Isn’t Everything

• Even low-paying or unpaid internships can provide the credibility, skills, and industry exposure needed to break into machine learning roles.
• Prioritize internships that get your hands dirty with real code, machine learning models, and teamwork.

2. Build a Stellar Resume—Referrals Optional

• Manoj notes: “My decent resume helped me secure my jobs at Meta and Amazon, as I did not have a reference for either.”
• Don’t be discouraged if you lack a network. Focus on:
  • Clear, impactful descriptions of your projects
  • Tangible results or metrics (improved algorithm accuracy, reduced server costs, etc.)
  • Links to code (GitHub/portfolio website)

3. Professional Experience > University Projects (after 2–3 years)

• Update your resume and LinkedIn as your experience matures. Once you have a couple of years in the workforce:
  • Remove long lists of university projects
  • Highlight industry impact and real business outcomes from professional roles and internships

4. Crack the Behavioral Interview

• Behavioral interviews are critical in big tech hiring—don’t neglect them. For example, Amazon employs tough six-round processes that include coding, machine learning, and behavioral questions.
• Manoj’s Method:
  • Study the company’s leadership principles and values in depth
  • Create a “story bank”: document comprehensive scenarios showcasing your skills, decision-making, teamwork, and leadership under pressure
  • Be ready with concise responses to follow-up questions

Industry Shifts: The Rise of Deep Learning at Meta and Beyond

According to Manoj, the AI/ML landscape has changed dramatically:

• Previously, engineers manually designed how data was represented (feature engineering) and selected algorithms accordingly.
• Today, deep learning allows machines to learn complex patterns directly from raw data, vastly improving predictive accuracy and automation potential.

This shift means that companies like Meta now place a premium not just on theoretical knowledge, but on practical experience deploying, optimizing, and scaling deep learning systems (using tools like TensorFlow, PyTorch, and cloud architectures).

Meta’s Environment: Job Titles and Team Dynamics

Manoj highlights that in cutting-edge tech, titles blur:

• You may be called a Research Scientist, Applied Scientist, Software Engineer, or Machine Learning Engineer, depending on the company and team.
• What matters is your ability to solve real-world problems with AI and to collaborate across engineering, product, and design.

Strategies for Aspiring Machine Learning Engineers—Inspired by Manoj

1. Focus on experience over prestige
   • Big names help, but meaningful contributions—even at startups or smaller firms—can provide better stories for your interviews and resume.
2. Don’t be afraid of “cold emailing”
   • If you lack a direct referral, research the hiring manager or recruiter, and send concise, personalized emails explaining your fit.
3. Master both technical and behavioral interviews
   • Dedicate equal effort to coding, system design, and soft skills. Use platforms like LeetCode, HackerRank, and practice mock interviews with peers.
4. Stay updated on industry trends
   • Read the latest blogs, research, and news on AI/ML. Experiment with emerging tools and contribute to open-source projects.

Common Pitfalls and How to Avoid Them

• Overemphasizing GPA or academic achievements once employed: Industry impact trumps grades after your early career.
• Poor behavioral interview prep: Always prepare stories aligned with company values and your major projects.
• Sticking with outdated skills: Constantly upgrade your toolkit and knowledge base (cloud computing, advanced neural architectures, generative AI, etc.).

What Does This Mean for Indian Tech Talent?

Manoj’s rise demonstrates that talented, determined engineers from India can make it to the top ranks of global big tech—without “insider” connections. The technology industry, especially AI/ML, is meritocratic; if you showcase your ability, initiative, and results, you can unlock extraordinary opportunities.

India continues to produce some of the world’s most sought-after engineers, researchers, and entrepreneurs. The ability to leverage internships, practical experience, and strategic preparation is now—more than ever—the key to joining elite firms like Meta, Google, Amazon, and Microsoft.

Conclusion: Charting Your Own Big Tech Success

Manoj Tumu’s journey from Amazon to Meta is not just a testament to personal grit, but a playbook for every aspiring software engineer, data scientist, and AI enthusiast. By combining practical experience, resume polish, and interview mastery, you too can transform your career, breaking into the highest echelons of the tech world.

Stay curious, stay humble, and never stop learning — your big break could be just an email, internship, or interview away.

FAQs

Q1: What’s the typical career path into companies like Meta or Amazon as a machine learning engineer?

• Answer: Most successful candidates start by pursuing internships (paid or unpaid) in AI/ML, followed by a strong academic or project portfolio. Securing an entry-level or junior role at a reputable tech company, demonstrating impact with business or product teams, updating your resume, and preparing for rigorous coding and behavioral interviews are all vital steps.

Q2: Do you need referrals or insider connections to get hired by big tech companies?

• Answer: No. As Manoj’s example shows, a strong resume and persistence (for example, through direct applications or cold emails) can succeed even if you have no inside contacts.

Q3: Are technical skills more important than soft skills for roles at Meta, Amazon, or Google?

• Answer: Both are equally crucial. Recruiting processes in big tech rigorously test coding and machine learning skills and your alignment with company values, teamwork, and communication. Prepare thoroughly for both to maximize your chances.

Are you inspired by Manoj’s journey? What else would you like to know about breaking into big tech or building a career in machine learning? Share your questions or thoughts in the comments!

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