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.
We look for research engineers who are excited to tackle unsolved problems. Progress is only as trustworthy as its measurement. As our model, data, and reasoning efforts multiply, every team needs to know, precisely and comparably, whether a change made the model better. Your mission is to build the central evaluation framework that the entire research organization runs on: the pipelines, the metrics, and the tools that turn results into shared understanding.
Responsibilities
Design and build a central, reusable evaluation framework that every model and every team runs through
Implement evaluation pipelines, benchmark suites, and baselines that make model quality measurable and comparable across efforts
Build the visualization and dashboard tools that turn raw results into shared, actionable understanding for the whole team
Establish sound statistical methodology for evaluation, so teams can distinguish real improvements from noise
Partner with research and domain teams to translate what "good" means in each domain into standardized, automated metrics
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 software engineering skills and experience building data or evaluation pipelines at scale
Experience turning research or model outputs into metrics, benchmarks, and visualizations that teams rely on
Solid grasp of probability and statistics, with the judgment to design evaluations that measure what they claim to
Full-stack range: comfortable building both backend pipelines and the frontend tools people read results in
Owns deliverables end-to-end, from collecting requirements to autonomously driving execution
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