NVIDIA executives provided career guidance for students and recent graduates seeking positions in the rapidly expanding artificial intelligence sector during the company's GTC global AI conference in March. The advice comes as more than 60% of students report AI's rise has affected their career plans, according to Inside Higher Ed survey data.
Eric Vargas, senior university recruiting manager at NVIDIA, emphasised that companies seek candidates who demonstrate technical skills, subject-matter expertise, and innovative thinking. "There isn't necessarily such a thing as an 'AI candidate,' but there are sub-areas within AI, different domains and industries that are going to be affected by AI," Vargas explained during the panel discussion.
The World Economic Forum forecasted in 2024 that global demand for AI and machine learning specialists will grow over 40% in coming years. NVIDIA's guidance addresses this expanding market opportunity across autonomous vehicles, robotics, gaming, and healthcare sectors.
Sadie St. Lawrence, founder of Women in Data and CEO of the Human Machine Collaboration Institute, advised students to integrate AI tools into daily workflows regardless of educational background. "Don't worry so much about necessarily the right skills — make sure you're integrating the use of AI in your daily workflow," St. Lawrence stated.
Carter Abdallah, senior developer technology engineer at NVIDIA, noted that diverse educational backgrounds create competitive advantages. "AI allows you to branch outside your area of expertise," Abdallah said. "AI can do some of the heavy lifting so nontechnical people can code, and technical people are now finding it valuable to lean into liberal arts education."
Saudia Jones, generative AI analyst at NVIDIA and recent San Jose State University graduate, emphasised passion-driven problem selection. "Pick a problem that seems impossible to solve but that you're passionate about," Jones advised. "Connect with 'why' you're driven by this, because that's going to be the thing that leads you to overcome the challenges and innovate."
NVIDIA's recruiting team highlighted proactive demonstration of capabilities as key differentiators. Examples include building robotics solutions with NVIDIA Jetson developer kits, publishing technical articles, or establishing AI-related campus organisations.
Organisations can leverage NVIDIA's Deep Learning Institute training programmes to develop internal AI capabilities. The guidance suggests companies should prioritise candidates who demonstrate practical AI integration skills across diverse professional backgrounds, supporting broader enterprise AI adoption strategies.
The 40% projected growth in AI specialist demand creates significant talent acquisition challenges for enterprises. NVIDIA's emphasis on interdisciplinary skills suggests companies should expand recruitment beyond traditional computer science backgrounds. Organisations investing in AI training programmes and mentorship networks will likely secure competitive advantages in attracting qualified candidates. The focus on responsible AI use and trustworthy AI development aligns with enterprise risk management priorities as companies scale AI implementations.