A Stanford professor and AI startup cofounder explains how to get a job at an AI company

"If you want to work in AI, you need to show that you can actually do the work. Launch real projects using public datasets, deploy a demo, post your work on GitHub, or write about it on a blog. Participate in hackathons - they're a fantastic way to demonstrate initiative and teamwork in a short time. We organize hackathons ourselves and are often impressed by what participants produce. It's concrete proof of what you can do."

"My second recommendation is to show adaptability - that you're the kind of person who is always experimenting with new tools, and that you can learn quickly. This is essential because AI is evolving at a pace that surprises even those of us who work in the field every day. The best job candidates have taught themselves frameworks like PyTorch, JAX, or LLM tooling, and they stay current on areas like GenAI, multimodal models, diffusion, and reinforcement learning."

AI job seekers should build and deploy real projects using public datasets, host demos on GitHub, or write blog posts to demonstrate practical skills. Participation in hackathons provides concrete evidence of initiative, teamwork and fast execution, and failures signal curiosity and proactivity while enabling learning through subsequent projects. Employers value ambition, problem-solving and curiosity, as shown by hires who built generative AI tools for customer-purchase analysis. Adaptability and rapid self-teaching of frameworks like PyTorch, JAX and LLM tooling are essential as AI advances quickly. Staying current on GenAI, multimodal models, diffusion and reinforcement learning matters more than fixed skill sets. Communication and empathy are as important as coding because the ability to explain technical work to nontechnical stakeholders determines impact.

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