Artificial Intelligence Researcher

Hiring researchers from world models, physical AI and model-based RL—up to $500k base + equity.

I’m partnering with a San Francisco AI research lab building foundation models that predict how complex physical systems evolve—and determine the actions required to change them.

Their first proving ground is one of the most complex, observable and consequential physical systems on Earth.

For researchers from world models, robotics, autonomous driving, generative modelling or multimodal learning, the technical overlap is significant:

• Learning from sparse sensors, point clouds, hyperspectral imagery and physical fields
• Developing stable, long-horizon rollouts
• Modelling high-dimensional physical systems under uncertainty
• Scaling novel architectures and training recipes across massive datasets
• Turning predictive models into systems capable of planning, control and decision‑making

This is not an incremental forecasting problem.

The ambition is to move beyond pattern recognition and build models that learn how interventions alter the physical world.

The founding team has worked across frontier AI research, autonomous systems, computational biology and particle physics. They are now making several hires across multimodal modelling, large-scale pretraining and model-based planning.

You do not need previous experience in weather or climate science.

They are looking for exceptional researchers who have trained serious models, understand experimentation deeply and want genuine ownership of open‑ended research problems.

San Francisco, onsite

Research ownership from architecture through scaled training

If your work sits anywhere across world models, sequence modelling, generative modelling, sensor fusion, model-based RL, planning or physical AI, message me directly for more information.

#World Models #AI Research #Physical AI