At just 23, Indian-American machine learning engineer Manoj Tumu has made a headline-grabbing career leap. After spending time at Amazon, he has joined Meta with a total compensation package exceeding $400,000, a move that highlights both his talent and the growing opportunities in artificial intelligence.
Speaking about the shift, Tumu explained that although Amazon gave him valuable learning experiences, Meta’s projects appeared more engaging. “As soon as I received the offer, I knew I wanted to accept it,” he said, reflecting on his decision to pursue work that excited him more.
Sharing advice for young professionals, Tumu underscored the importance of real-world exposure over academic or personal projects. He urged students to pursue internships during college, even if they are unpaid or low-paying, as the experience carries far more weight on resumes. He added that once someone gains two or three years of professional experience, personal projects can be replaced with actual work achievements.
Tumu also emphasized that he did not rely on referrals to land jobs at Amazon or Meta. Instead, he applied directly through company websites and LinkedIn, proving that a strong resume and clear presentation of skills can stand out on their own.
On the interview process, he highlighted that preparation—especially for behavioral rounds—is crucial. At both Amazon and Meta, his interview process included coding, machine learning tasks, and behavioral assessments. To prepare, he studied each company’s values and even kept a detailed document of potential questions with structured answers, ensuring he was ready for follow-ups.
Reflecting on his own path, Tumu admitted he missed out on internships during his college years. However, he later secured a contract role after graduation and made an intentional decision to pursue machine learning instead of traditional software engineering, despite lower initial pay. This choice eventually paid off, leading him to Meta’s advertising research team.
Looking at the broader AI landscape, he noted the ongoing transition from classical approaches, where humans selected features, to deep learning, which leverages neural networks to automatically extract insights from raw data. He also pointed out that job titles in AI differ across companies, with roles spanning applied scientist, research scientist, machine learning engineer, and software engineer.
For aspiring engineers, Tumu’s journey serves as a lesson in perseverance, calculated risk-taking, and the value of preparation.
