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Numerical Weather Prediction Jobs (NOW HIRING)

Pravāh

AI Weather Scientist

San Francisco, CA · On-site

$150K - $250K/yr

Run numerical weather prediction models to generate high-resolution forecasts and training data. * Inform the development of AI weather forecasting models and innovate on existing architectures.

Jane Street

Senior Weather Analyst/ML Researcher

New York, NY · On-site

$126.90K - $127.50K/yr

Expertise in relevant topics such as: sub-seasonal predictability, data assimilation and numerical weather prediction, tropical variability, synoptic meteorology, renewable energy forecasting

Axiom Consultants

Chief Software Engineer

Rockville, MD · Remote

The EPIC Program and Contract will deliver world-class numerical weather prediction systems supporting NOAA's mission to save lives, protect property, and enhance the economy. EPIC is a facilitating ...

Axiom Consultants

Chief Systems Architect

Rockville, MD · Remote

The EPIC Program and Contract will deliver world-class numerical weather prediction systems supporting NOAA's mission to save lives, protect property, and enhance the economy. EPIC is a facilitating ...

Axiom Consultants

IDIQ Project Manager

Rockville, MD · Remote

The EPIC Program and Contract will deliver world-class numerical weather prediction systems supporting NOAA's mission to save lives, protect property, and enhance the economy. EPIC is a facilitating ...

Mantari

Software Engineer

Long Beach, CA · On-site +1

Experience with numerical weather prediction, remote-sensing data, or geospatial intelligence * Background in physics-informed ML, simulation modeling, or data assimilation * Contributions to open ...

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Numerical Weather Prediction information

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$51.5K

$100.1K

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How much do numerical weather prediction jobs pay per year?

As of May 30, 2026, the average yearly pay for numerical weather prediction in the United States is $100,142.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,500.00 and $112,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Numerical Weather Prediction Scientist, and why are they important?

To excel as a Numerical Weather Prediction (NWP) Scientist, you need a strong background in meteorology, mathematics, and computer science, often supported by an advanced degree in a related field. Familiarity with programming languages like Python or Fortran, experience with high-performance computing, and knowledge of atmospheric modeling systems such as WRF or ECMWF models are typically required. Analytical thinking, problem-solving ability, and effective teamwork are crucial soft skills for interpreting complex data and collaborating across disciplines. These competencies are vital for developing accurate weather forecasts and advancing predictive capabilities that impact public safety and resource planning.

What are the main challenges faced by professionals working in Numerical Weather Prediction (NWP)?

Professionals in Numerical Weather Prediction often encounter challenges such as handling large volumes of meteorological data, optimizing complex models for accuracy, and keeping up with advances in computational technology. Collaboration with meteorologists, data scientists, and computer engineers is common to ensure that models are both accurate and efficient. Additionally, NWP specialists are expected to interpret model outputs for practical forecasting, requiring strong analytical skills and attention to detail. Continuous learning and adaptation are key as the field evolves rapidly with new research and technological developments.

What is numerical weather prediction?

Numerical Weather Prediction (NWP) is the use of mathematical models and computer simulations to forecast the weather. These models use current observations of the atmosphere, oceans, and land surface to solve equations describing atmospheric physics and dynamics. By running these models on powerful computers, meteorologists can predict weather conditions from hours to several days or even weeks into the future. NWP models are essential for modern weather forecasting and are used by meteorological agencies worldwide.
More about Numerical Weather Prediction jobs
What cities are hiring for Numerical Weather Prediction jobs? Cities with the most Numerical Weather Prediction job openings:
What states have the most Numerical Weather Prediction jobs? States with the most job openings for Numerical Weather Prediction jobs include:
What job categories do people searching Numerical Weather Prediction jobs look for? The top searched job categories for Numerical Weather Prediction jobs are:
Numerical Weather Prediction jobs near you
Infographic showing various Numerical Weather Prediction job openings in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Temporary. Highlights an 100% Physical job distribution, with an average salary of $100,142 per year, or $48.1 per hour.

AI Weather Scientist

Pravāh

San Francisco, CA • On-site

$150K - $250K/yr

Full-time

Posted 3 days ago

Job description

About Pravāh
Pravah is building foundational intelligence for the electric grid. We apply modern machine learning to complex physical infrastructure problems spanning grid operations, weather, and geospatial systems.
Our work sits at the intersection of computer vision, physical systems, and large-scale ML, with deployments across utilities in the United States and India. We leverage multimodal data - including satellite imagery, LiDAR, and street-level data - to build high-fidelity representations of grid assets and their surroundings.
We are backed by Khosla Ventures, Pear VC, and Conviction.
To know more about who we are, what we are building, and why we are excited read this Notion! https://pravah.notion.site/
The role
We are hiring an AI Weather Scientist to advance the next generation of weather forecasting systems. You will work closely with machine learning and software engineers on four core threads:
  • Run numerical weather prediction models to generate high-resolution forecasts and training data.
  • Inform the development of AI weather forecasting models and innovate on existing architectures.
  • Evaluate pre-trained global and regional models against reanalysis, satellite, and ground observations to identify areas for improvement.
  • Procure, process, and create ML-ready global and regional weather datasets, with explicit focus on data-sparse regions.

What you'll work on
  • Drive the development of next-generation multiscale, regional, and global weather forecasting systems, and their benchmarking against reanalysis and observations, especially during extreme events and over data-sparse regions.
  • Tailor weather prediction models to sector-specific needs: energy (solar and wind demand/generation, grid stress), agriculture (seasonal outlooks, crop-relevant variables), and extreme-weather resilience (heatwaves, heavy precipitation, tropical and extratropical cyclones, convective storms).
  • Assess the applicability of state-of-the-art AI methodologies including foundation models, generative architectures, and physics-informed ML to weather and climate forecasting.
  • Work at the intersection of physics-based modeling and machine learning: hybrid physics-ML approaches, learned parameterizations, and emulators.

Who you are
Any combination of the following will strengthen your application. We do not expect you to have all of them.
Preferred qualifications*
  • A master's or PhD in geophysical sciences, physics, applied mathematics, computer science, statistics, or a related field. A bachelor's with 7+ years of relevant research or operational experience is also acceptable.
  • Demonstrated depth in either numerical weather prediction, meteorology, or earth system modeling through research projects, publications, model contributions, or operational work.
  • Experience working with high-dimensional observational and modeling datasets (reanalysis products, satellites, weather stations) in forecasting
  • Experience working with deep learning models and familiarity with at least one framework (PyTorch, JAX, or TensorFlow).*
  • Excellent written and verbal communication, including the ability to explain technical work to both domain experts and cross-disciplinary collaborators.

Nice-to-have
  • Hands-on experience with high-resolution regional earth-system models such as WRF or MPAS, including dynamical cores, physics parameterizations, boundary-layer and convection schemes, or coupled ocean-atmosphere configurations.
  • Experience with operational forecasting models or workflows (real-time data ingest, verification, cycling, product generation).
  • Experience with either of data assimilation, ensemble and probabilistic forecasting, convection-permitting or mesoscale modeling, regional downscaling, or subseasonal-to-seasonal (S2S) prediction.
  • Experience using or building AI weather prediction models - whether benchmarking, fine-tuning, or extending them. Applying generative AI and diffusion models to weather and climate is a strong plus.
  • Publications in leading atmospheric, oceanic, or climate science venues and/or major ML/AI conferences.

What you'll gain
  • Ownership of weather forecasting models deployed for real-time applications.
  • Experience working on hard, open-ended problems at the intersection of AI and physical infrastructure.
  • Ability to shape technical direction and shape the frontier of AI-weather prediction revolution.
  • Close collaboration with a deeply technical founding team.
Why this role
This role sits at the frontier of the AI-weather revolution, applying modern machine learning to earth system modeling. The next decade of progress in weather and climate prediction will be built by scientists who understand the physics and the data and have learned to wield generative AI. You will be working in data-sparse regions where data is heterogeneous, ground truth is incomplete, and progress requires both technical depth and first-principles thinking.

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