He helped build Google Cloud's Vertex AI, then co-founded Fireworks AI to make open models run at production scale. His through-line is the difficult engineering between an impressive model and an answer that arrives on time.

He helped a Google team rethink how machines read a sentence. Nine years later, after two startups and an open model release, the question of who gets to build on that idea still follows him.

At ETH Zürich, Ce Zhang asked why useful machine learning stayed out of reach for so many people. As Together AI’s founder and CTO, he is still working on the same problem - now at cloud scale.
Before he worked on the machinery behind generative AI, Dmytro Dzhulgakov was a Kharkiv student who wrote code for an Intel 8080 and competed against the world’s fastest programmers. At Fireworks AI, the contest has changed, but the clock still matters.

After helping build AlphaStar, Grok and the machinery behind xAI, the physicist turned founder is asking a more personal question: who gets to shape the intelligence we use?

Inside Meta, rising demand for machine learning compute made the future feel less like a prediction than a capacity meeting. Chen left to co-found Fireworks AI in 2022; now his argument is that useful AI must be affordable to run and specific to the job.

Before she led PyTorch and founded Fireworks AI, Lin Qiao learned to read cargo-ship blueprints beside her father. Her career has followed the same discipline ever since: absorb the complexity, expose the useful parts, and build for people who need the system to hold.