Member of Technical Staff
Causal Labs
Member of Technical Staff
Causal Labs
Trains physics AI models to forecast weather
Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.
A model only matters if it changes what happens in the real world. Our deployment teams embed directly with the institutions that make the highest-stakes decisions about physical systems. They turn our models into results those institutions can depend on and carry everything we learn in the field back into the product. Deployment works in small, cross-functional pods and partners closely with Product Engineering, which builds the platform the pods deploy.
We look for engineers who are excited to tackle unsolved problems. They thrive in high stakes situations, sitting next to the people in the field who depend on the answers. You embed with the institutions using our models to make consequential decisions about the physical world. Your mission is to make sure we build the right solution for a customer's mission: delivering the data infrastructure, integrations, and AI systems that work in practice, not just in theory — and expanding our core product to solve new problems as you discover them.
Willingness to travel to and spend extended time on-site with customers is required.
Responsibilities
Embed with strategic customers to understand their mission and ensure we build the solution that actually solves it
Deliver scalable data infrastructure and integrations inside real customer environments
Design and ship AI systems that hold up under real-world conditions, data, and constraints
Surface new problems as you find them in the field, and extend our core platform to handle them
Work hands-on across the stack — technical, resourceful, and close to the user
What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
Strong, versatile engineering skills and the resourcefulness to solve unfamiliar problems with whatever it takes
Experience building and shipping real systems in production — data infrastructure, integrations, applications
Comfort working directly with customers and adapting fast to their environments and constraints
Willingness to travel and embed on-site wherever the mission needs you
A bias toward real-world impact over elegance: solutions that work for the user, under pressure
Trains physics AI models to forecast weather
Causal Labs is an AI research company building a foundation model of physical systems for industries whose operations turn on the weather. The company trains on atmospheric sensor and observation data rather than text or images, a domain where cause and effect are governed by physics and yesterday's prediction can be scored against what happened. It runs early pilots with commercial partners and pairs researchers with engineers who deploy the models on customer data. Unlike conventional forecasters, which report what conditions are likely, it is also trying to identify which interventions would change an outcome. The company's goal is to make AI reason about cause and effect by grounding it in the most heavily observed physical system available.
Explore more opportunities at Causal Labs