- AI scientists are in high demand, and their salaries can reach over $500k, says Ashwyn Sharma, who has worked at Meta and Saleforce.
Pursuing a job in artificial intelligence may be one of the smartest moves you can make in 2025, with careers in the field among the fastest-growing in the entire country.
But people like Ashwyn Sharma, who's had a decades-long career as an AI scientist, saw the wave coming years ago, and his skills are in high demand. So much so that companies are willing to dish out hundreds of thousands of dollars in compensation packages—making the career path one of the highest paid in the entire job market.
“When LLMs in the whole ChatGPT revolution started, and these roles started to break out, I remember some roles getting a lot of attention,” Sharma tells Fortune. “I don't know if it was Netflix or OpenAI, but they had roles paying up to a million dollars, or 750k annually—and that's just the base salary.”
Sharma’s career has taken him to multiple tech companies, including Google, Meta, and Salesforce, but he says nothing could have prepared him for the period we are in today, in which every company is prioritizing AI.
“I've been involved in this space before it was cool,” says Sharma, who now works at Otter.ai as a senior research scientist.
At the time of writing, companies like Apple, Amazon, and Meta are actively seeking AI scientists and offer salary starting ranges of over $200k. When all is said and done, Sharma says it is not an uncommon practice for AI scientists and researchers to have compensation packages of over $500,000—with just five to 10 years of work experience.
An almost guaranteed six-figure career—if you’re up for the challenge
Sharma and other AI scientists bring home more money than some doctors and lawyers—and at a significantly lower cost in terms of time and tuition dollars. However, becoming an AI scientist doesn’t happen overnight.
After obtaining a bachelor’s in electronics and communications engineering in India, Sharma secured a role at Groupon, working on its machine learning team. But he truly became hooked on AI after realizing that it could find the “needle in the haystack” among datasets.
“I truly saw the potential for AI to really model and understand high dimensional data,” he says.
He later went back to school at Georgia Tech to obtain a master’s in computer science, and the rest is history. Sharma became a deep learning software engineer at Salesforce and became one of the lead developers in creating the company’s Einstein AI products. Later, he would land a role at Facebook on its AI team. And this was all before ChatGPT was even released.
Since generative AI came into the mainstream two years ago, the supply and demand of AI roles have increased dramatically. Seeking to stay ahead of the curve, companies massively upscaled their AI-related jobs, but at the same time, aspiring tech leaders pounced on the opportunity to be part of the next wave of innovation.
“Ever since ChatGPT came out, more companies and more organizations are developing these systems, so there are more roles out there, for sure,” Sharma says. “I think there is no shortage of research scientist roles out there, in my opinion, but the bar is also high at the same time.”
In order to land a six- or even seven-figure salary, Sharma says a degree in computer science is the foundation—with most AI scientists having a master’s or doctorate. However, it’s not uncommon to gain industry experience and transition into a research role.
“I wouldn't say that like you absolutely need to have a graduate degree and need to go to grad school and work with the top professors and the top AI labs,” he says.
The open-source community has helped democratize AI development, he adds. There is a plethora of resources online, from simple lectures on YouTube to longer lessons on platforms like Coursera.
And despite AI being one of the buzzwords of the last few years—with ChatGPT urging a new era for workers, educators, and other spaces—we’re still only at the beginning, Sharma says.
“I really hope that in general we find the slam dunk use case, like the iPhone moment for AI,” he says. “That's still yet to come.”
