Machine Learning Scientist - 140859

UC San Diego; San Diego (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

UC San Diego CW3E is seeking a scientist to develop and apply AI weather prediction models, focusing on extreme weather and water events. The role includes architecture design, ensemble methods, and potential deep learning post‑processing for QPF and related variables.

The candidate will communicate results through publications, conferences, and reports, and may contribute to proposals and platform improvements. Collaboration with staff, faculty, researchers, and students is essential.

Qualifications

• Bachelor's degree in meteorology, atmospheric sciences, climate science or related field. Master's degree or PhD preferred.
• Experience in implementing machine learning methods for weather/climate research, analysis of dynamical model outputs, and publishing research results.
• Experience in handling artificial intelligence weather models and numerical weather prediction model simulations, and developing methods for deterministic and probabilistic predictions of hydrometeorological variables.
• Knowledge of operational forecasting models and products from NOAA and ECMWF. Knowledge of observational and reanalysis data sets relevant to US West coast meteorology and climate.
• Strong experience in scientific programming, Unix environment, and scripting languages such as Python, R, or Matlab.
• Experience using common ML software (TensorFlow, Keras, PyTorch, Scikit-Learn) on cloud computing environments (AWS, Azure, etc.).
• Skills to communicate complex information in writing and presentations; experience presenting at scientific conferences.
• Excellent interpersonal skills and ability to work independently or in a team.

Responsibilities

• Develop AI-based weather prediction models and novel architecture designs for CW3E objectives.
• Explore ensemble strategies and deep learning–based post-processing for QPF and related variables.
• Publish findings in peer‑reviewed journals and present at conferences and meetings.
• Contribute to proposals and strategic growth of computing platforms and data management.

Skills

Machine learning

Python

Research communication

Statistical analysis

Unix environment

Publications

Team collaboration

Education

Bachelor's in meteorology or related field

Master's or PhD preferred

Tools

TensorFlow

Keras

PyTorch

Scikit-Learn

AWS

Azure

Job description

DESCRIPTION

The CW3E machine learning team is recruiting a scientist to work on the development of artificial intelligence (AI) weather prediction models. The successful candidate will apply their background and expertise in computational science to develop, support, and execute projects of broad scope and complexity that address CW3E's objectives, with a focus on modeling, analyzing, and predicting extreme weather and water events.

The position will contribute directly to ongoing developments at CW3E in the domain of AI weather prediction, including novel architecture design and ensemble strategies. May also develop innovative deep learning-based post-processing methods for quantitative precipitation forecasting (QPF) as well as forecasting other relevant variables, e.g., temperature, integrated water vapor transport (IVT) for the benefit of water management.

Communicates research findings through peer-reviewed journal publications, conference presentations, technical reports, and other publications, as needed. May give technical presentations to associated research and technology groups and management, and represent the organization at national and international meetings, conferences, and committees. Supports proposal development and contributes to ongoing efforts on the strategic growth of computing platforms and improvement of data management procedures.

QUALIFICATIONS

• Bachelor's degree in meteorology, atmospheric sciences, climate science or related field. Master's degree or PhD preferred.
• Experience in implementing machine learning methods for weather/climate research, analysis of dynamical model (e.g., WRF) outputs, and publishing research results.
• Experience in handling artificial intelligence weather models and numerical weather prediction model simulations, and developing methods for deterministic and probabilistic predictions of hydrometeorological variables.
• Knowledge of operational forecasting models and products from NOAA and ECMWF. Knowledge of observational and reanalysis data sets relevant to US West coast meteorology and climate.
• Thorough skills associated with statistical analysis and systems programming. Strong experience in scientific programming, working in a Unix environment, and with scripting languages such as Python, R, or Matlab is highly desirable.
• Experience using common machine learning software (Tensorflow, Keras, PyTorch, Scikit-Learn, etc.) on cloud computing environments (AWS, Azure, etc.).
• Skills to communicate complex information in a clear and concise manner both verbally and in writing. Skills in scientific writing for peer-reviewed journal publications, scientific graphical representation, and experience presenting at scientific conferences (oral and poster presentations).
• Thorough skills in analysis and consultation.
• Thorough knowledge of research function. In-depth experience as an independent researcher.
• Skills in project management. Strong time management skills. Demonstrated ability to prioritize tasks and meet deadlines.
• Research skills at a level to evaluate alternate solutions and develop recommendations. This includes proposing new analyses, new conceptual ideas, or new workflow recommendations.
• Excellent interpersonal skills including thoughtfulness, diplomacy and flexibility with the ability to work independently or within a team framework in conjunction with principles of community with staff, faculty, researchers, and students.

SPECIAL CONDITIONS

• Job offer is contingent upon satisfactory clearance based on Background Check results.

Pay Transparency Act

Annual Full Pay Range: Unclassified - No data available (will be prorated if the appointment percentage is less than 100%)

Hourly Equivalent: Unclassified - No data available

Factors in determining the appropriate compensation for a role include experience, skills, knowledge, abilities, education, licensure and certifications, and other business and organizational needs. The Hiring Pay Scale referenced in the job posting is the budgeted salary or hourly range that the University reasonably expects to pay for this position. The Annual Full Pay Range may be broader than what the University anticipates to pay for this position, based on internal equity, budget, and collective bargaining agreements (when applicable).