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Meteorology Phd Jobs (NOW HIRING)

Waymo

Staff Data Scientist, Weather

San Diego, CA · On-site

PhD in a quantitative, weather-adjacent field (Atmospheric Science, Meteorology, Hydrology, etc.), and/or academic experience developing and applying statistical methods in those fields. * Experience ...

The Pennsylvania State University

... and meteorology. Also, you will use RF propagation models to analyze the effectiveness of ... A Masters' degree or PHD in Mathematics or Electrical Engineering Your working location can be a ...

Tuskegee University

... aviation safety, aviation meteorology, aircraft systems, aerodynamics, aircraft structures ... Earned MS (for instructor/assistant professor), PhD (for associate professor) in Aerospace ...

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How much do meteorology phd jobs pay per year?

As of Aug 17, 2026, the average yearly pay for meteorology phd in the United States is $96,278.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $98,500.00 per year, depending on experience, location, and employer.

How many years is a meteorology PhD?

A meteorology PhD typically takes about 4 to 6 years to complete after earning a bachelor's degree. The program involves coursework, research, and a dissertation, and duration can vary based on the student's progress and research complexity.

What can you do with a meteorology PhD?

A meteorology PhD prepares individuals for research, forecasting, and atmospheric science roles in government agencies, research institutions, and private companies. Graduates often work as climate scientists, weather modelers, or atmospheric researchers, utilizing skills in data analysis, computer modeling, and remote sensing tools.
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Meteorology Phd jobs near you
Infographic showing various Meteorology Phd job openings in the United States as of August 2026, with employment types broken down into 77% Full Time, 21% Part Time, and 2% Contract. Highlights an 73% Physical, 4% Hybrid, and 23% Remote job distribution, with an average salary of $96,278 per year, or $46.3 per hour.

Member of Technical Staff - Research, Atmospheric Science

Causal Labs

San Francisco, CA • On-site

Full-time

Posted 27 days ago

Job description

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 domain experts who are excited to tackle unsolved problems. Weather is our first proving ground - the most well-observed physical system on Earth - and getting it right demands deep atmospheric expertise embedded directly in the research. Your mission is to bring that expertise to bear on every part of the model: what data we learn from, how we know the model is correct, and where it still falls short.
Responsibilities
  • Guide the sourcing and validation of atmospheric data, advising on observation systems, their characteristics, and their pathologies
  • Define what forecast quality means, bringing rigorous verification methodology to how we evaluate the model
  • Run case studies on high-impact events to probe model behavior and surface failure modes
  • Benchmark against operational numerical weather prediction baselines and the state of the field
  • Partner with model, evaluation, and product teams to translate atmospheric expertise into research direction and credible results

What we're looking for
We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
  • Deep expertise in atmospheric science, meteorology, or a closely related field (typically a PhD or equivalent research experience)
  • Familiarity with operational forecasting, numerical weather prediction, and forecast verification methods
  • Comfort working with large observational and reanalysis datasets
  • Ability to collaborate closely with ML researchers and translate domain knowledge into technical requirements
  • A rigorous, evidence-driven approach to evaluating model quality

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