Rejected once, hired later: How this Indian student won an offer from Nvidia

Sylendran Arunagiri began planning his move from India to the United States in 2023, the advice he received was cautious. Friends and mentors warned that the American tech hiring cycle had slowed and that international graduates were finding it harder to secure roles.

He chose to proceed anyway. His plan was structured and time bound. Enrol in a master’s programme in product management at Carnegie Mellon University, secure a summer internship in 2024, and use that pathway to enter the artificial intelligence ecosystem. The long term target was Nvidia, a firm he associated closely with the AI infrastructure he wanted to work on,

reports.

Arunagiri arrived at a moment when the US technology labour market was recalibrating. Job postings had fallen from the surge seen during the pandemic years.

Layoffs across large firms increased competition for fewer openings.

According to

, companies were hiring at one of the slowest rates seen since 2013 as businesses adjusted to economic uncertainty and early adoption of generative AI tools.

For Arunagiri, the shift was not only economic but structural. He had studied in India within institutions that relied heavily on campus placement systems, where employers recruit directly from universities. The American system required a different approach.

“Job fairs were often more like networking events than recruiting opportunities,” he told

adding that in the US hiring environment, “You’re completely on your own. ”

Arunagiri began applying for internships before relocating to the US because many technology companies conducted interviews months in advance. In November 2023, he secured an interview with Nvidia.

The process appeared promising. He became confident enough to reduce applications elsewhere, assuming the opportunity would materialise.

After completing a final round in February 2024, however, he was rejected.

“I had to start from scratch, but by then many of the applications had dried out,” he told

The episode shows a recurring risk in competitive hiring cycles. Candidates often concentrate on one perceived breakthrough opportunity, only to find that timelines elsewhere have already closed.

Arunagiri eventually secured an AI product manager internship at Informatica in California.

The role provided industry exposure, but he continued analysing why his Nvidia application had failed.

He later concluded that timing and presentation had affected the outcome. He had attended the decisive interview while unwell and believed his energy level did not reflect his usual engagement.

“I came off as a dull candidate, but I’m usually energetic and conversational,” he told

. “I should have probably postponed it to a day that I was feeling better. ”

Rather than treating the rejection as final, he attempted to understand the company’s expectations. He contacted a human resources representative at Nvidia, who suggested connecting with current employees to better understand ongoing work and required skills.

Arunagiri spoke with several Nvidia interns and employees to map how teams were using AI tools and what kinds of product thinking were valued internally.

These conversations led him to begin building small independent projects aligned with those themes.

He shared this work publicly through professional platforms, documenting experiments with generative AI models and product use cases. The intention was to demonstrate applied understanding rather than rely solely on formal credentials.

This approach mirrors a broader change in tech hiring, where portfolios and demonstrable experimentation increasingly supplement traditional resumes.

After completing his internship, Arunagiri resumed his job search ahead of graduating in December 2024. As an international student on a F-1 visa, he faced a fixed timeline. Without employment within 90 days of graduation, he would have had to leave the US.

In September 2024, he submitted a new application to Nvidia for a technical product marketing role focused on agentic AI. Around the same time, he also entered the interview process for a product management position at Microsoft.

Both processes advanced simultaneously. Within days of completing his degree requirements, he received offers from the two companies. He accepted Nvidia’s offer, citing role alignment and compensation considerations.

During the later interview rounds, Arunagiri said hiring managers had reviewed the projects he shared online. The visibility of ongoing work signalled initiative and familiarity with emerging tools.

He told

that candidates should avoid relying only on applications and instead present tangible evidence of their interests. “You need to find something that sets you apart from others,” he said.

He also emphasised the importance of managing time during a prolonged search and resisting comparisons with peers whose outcomes may be shaped by different circumstances.

Arunagiri’s experience captures a transition affecting many international students entering the US labour market.

Structured campus recruitment has given way to decentralised hiring processes that reward persistence, visibility, and alignment with rapidly evolving technologies.

The shift does not necessarily close opportunities. It redistributes how they are accessed. Success depends less on a single recruitment cycle and more on sustained positioning across months or years.

Arunagiri’s path from rejection to employment shows that adjustment. The outcome changed not because the market became easier, but because his strategy became more closely matched to how companies now evaluate potential hires.