A 23-year-old Indian-American engineer, Manoj Tumu, is making headlines for his rapid rise in the world of artificial intelligence (AI). Tumu works as a machine learning engineer at Meta, where his compensation exceeds $400,000 (₹3.36 crore). He worked at Amazon before joining Meta, but he picked the social media behemoth because of its research-driven culture.
In a detailed essay for Business Insider, Tumu explained how he cracked the highly competitive hiring process and shared advice for students and job seekers who want to enter the AI field.
AI Jobs Are Changing Rapidly
Tamu described how deep learning driven by neural networks has replaced standard approaches in the field of machine learning. In addition to increasing industrial competition, the introduction of ChatGPT and other similar tools has led to the creation of new job titles, including research scientist, applied scientist, and machine learning engineer.
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At Meta, Tumu’s role combines both research and application, ensuring the company stays at the forefront of AI innovation.
Why Work Experience Beats Projects
One of Tumu’s strongest messages is clear: real-world work experience matters more than student projects. “While projects are useful early on, they should eventually take a back seat,” he said.
He advised students to focus on internships during college because they carry more weight in job applications. By the time he applied to Amazon and Meta, he had completely removed projects from his résumé, instead highlighting his professional work experience.
How He Cracked Meta’s Interview
Tumu revealed that he never relied on referrals but instead applied directly through LinkedIn and company websites, relying on a strong résumé to stand out.
He also cautioned candidates against going unprepared into behavioural interviews, calling it a common mistake. He recommended aligning answers with company principles, just as he did with Amazon’s leadership principles and Meta’s corporate values.
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The Meta hiring process took nearly six weeks, including a screening call followed by four to six rounds of coding, machine learning, and behavioural interviews.
Choosing Passion Over Pay Early On
Reflecting on his early career, Tumu admitted he missed an internship in college but later secured a contract role. He picked machine learning because it matched his interests when he had to choose between a higher-paying software engineering role and a lower-paying machine learning role.
That choice, he said, opened the doors to bigger opportunities—eventually leading to Meta.
