Manoj Tumu, a 23-year-old Indian-American engineer, recently made headlines after leaving his lucrative job at ,[object Object], to join ,[object Object], for a machine learning role with a compensation package exceeding $400,000. Manoj’s journey into big tech wasn't straightforward; after completing his master's in AI, he took on a lower-paying contract role in machine learning that piqued his interest more than a higher-paying software engineering job. His engineering career took off at ,Amazon,, where he worked for nine months, gaining valuable real-world experience that later helped him secure the coveted role at ,Meta,. Manoj's career choices reflect a clear preference for growth and research opportunities over just salary figures, highlighting the importance of passion in the fast-evolving ,[object Object], .

In sharing advice for students and aspiring professionals, Manoj emphasizes the critical role of practical experience over academic projects on résumés. He advises students to actively pursue internships during their college years, regardless of the pay, because real-world work experience holds much more value in job applications. Manoj mentions that once candidates accumulate two to three years of professional experience, they can confidently eliminate personal projects from their resumes and focus on showcasing actual work. His personal application strategy involved no referrals; instead, he applied directly through company websites and LinkedIn, relying on a strong résumé and relevant experience to stand out to recruiters .

Preparation for interviews, especially behavioral rounds, is another cornerstone of Manoj’s advice. He believes many candidates overlook this aspect, which is a significant mistake. During his interviews at ,Amazon, and ,Meta,, Manoj faced multiple rounds that tested coding, machine learning knowledge, and behavioral competencies. To succeed, he extensively studied the companies’ core values and prepared detailed personal stories and answers tailored to those principles. This rigorous preparation process helped him navigate both coding challenges and cultural fit assessments, boosting his chances of success in competitive hiring environments .

Manoj also notes the rapid changes in the AI and machine learning landscape, highlighting the shift from classical machine learning techniques towards deep learning powered by neural networks. The rise of AI tools like ,[object Object], has intensified competition and diversified job titles, from machine learning engineer to applied scientist and research scientist. His ,Meta, role blends research and implementation, focusing on staying ahead of industry innovations. Manoj’s career trajectory and advice underscore how adaptability, continuous learning, and strategic career moves can open doors to leading roles in AI, inspiring many young engineers to prioritize growth and passion in their career paths .