Episode 83
How To Switch From Software Dev to Machine Learning Engineer (Amazon SDE -> Tiktok MLE POV) - w/ Umang

In this episode of Ready Set Do , my guest is Umang Chaudhary , a Machine Learning Engineer at TikTok and former Applied Scientist at Amazon . Umang’s story is one of momentum — a reminder that you don’t need decades of experience to reach the top tiers of tech.
Who this is for
- You are breaking into a new lane and need proof before the story looks obvious.
- You would rather hear Umang's version while the mess is still fresh than get another polished hindsight sermon.
Key takeaways
- Switch From Software Dev to Machine Learning Engineer (Amazon SDE -> Tiktok MLE POV) - w/ Umang
- Umang's first recommendation to prospective international students is to do their own due diligence instead of...
- He remained bullish on the United States because of its consumer market, company density, innovation, and cultural...
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Umang's first recommendation to prospective international students is to do their own due diligence instead of outsourcing a life-changing decision to a consultancy. Moving countries is expensive to reverse and consumes formative years. Advisers can contribute information, but their incentives and risk are not the student's, so university, cost, curriculum, immigration, and employment assumptions need independent verification.
He remained bullish on the United States because of its consumer market, company density, innovation, and cultural accessibility for English-speaking students. That is Umang's personal 2025 view, not a guarantee about current hiring or immigration. His practical condition is that students arrive with clear semester goals, consistent effort, and enough flexibility to adapt when the original plan fails.
Umang suggests sequencing a master's deliberately. A student targeting an internship might take a lighter first-semester load while applications and interview preparation peak, then take harder courses after securing an offer. The right sequence depends on program rules and academic needs, but his point is to treat course selection, recruiting, and skill development as one constrained calendar rather than unrelated tasks.
At UMass Amherst, his computer-science coursework was harder than expected. Some assignments consumed nearly a week. Instead of interpreting the struggle as proof that he did not belong, he reallocated attention toward internships and opportunity creation. He had arrived directly after his bachelor's without full-time experience and knew he would compete against students who already had two or three years of work.
That meant monitoring where classmates applied, identifying recruiters, attending career fairs and networking events, and following up repeatedly. By the end of his first semester, Umang had internship offers from Dell and Amazon and had interviewed with Google. He presents those outcomes as the result of aggressive outreach and preparation, not evidence that every student who copies the volume will receive the same offers.
Show notes
In this episode of Ready Set Do , my guest is Umang Chaudhary , a Machine Learning Engineer at TikTok and former Applied Scientist at Amazon . Umang’s story is one of momentum — a reminder that you don’t need decades of experience to reach the top tiers of tech. What matters more is the mindset: continuously learning, adapting fast, and being ready to leap when opportunity strikes. We dive deep into how Umang built his way into machine learning roles in Big Tech without prior ML experience , and the exact principles that helped him accelerate his career both internally at Amazon and externally to TikTok.
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