> Markdown version of [/jobs/ext/3964464-physical-scientist-seasonal-forecast-system-developer](https://www.wearedevelopers.com/jobs/ext/3964464-physical-scientist-seasonal-forecast-system-developer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Physical Scientist - Seasonal Forecast System Developer - **Company:** Lynker Technologies - **Location:** College Park, MD, United States - **Experience:** Starter - **Salary:** $75,000.0 - **Contract:** Temporary contract - **Skills:** Agile Methodology, Artificial Intelligence, Fortran (Programming Language), Github, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Systems Development Life Cycle, Software Organization, High Performance Computing, Software Version Control, Programming Languages - **Published:** October 27, 2026 - **Apply:** https://www.jofdav.com/jobs/60261637-physical-scientist-seasonal-forecast-system-developer ## About the Role A Master or PhD's Degree or equivalence in atmospheric sciences or meteorology, oceanic, mathematics, or related physical science with 1-2 years of experience in the area of global coupled ensemble forecast system development, sub-seasonal to seasonal prediction, numerical prediction, and/or AI/ML foundational prediction models. Candidates must be a U.S. Citizen or Permanent Resident Card Holder to qualify (please indicate in your cover letter). Demonstrated knowledge, skills, and abilities in the following areas: * Advanced knowledge of S2S prediction and experience in developing ensemble based and coupled SFS for research and operational implementation. * Development of AI-ML methods for NWP and/or geophysical applications. * Excellent knowledge and skills of modern programming languages and object-oriented languages, such as FORTRAN and Python. * Skills and knowledge of the High Performance Computing (HPC) environment with strong scripting capabilities. * Proficient with modern software development practices and version control software (e.g., Git and GitHub). * Demonstrated skill in communicating effectively with scientists of diverse backgrounds on technical details of the work plan and present results accurately and clearly in both oral and written form. * Ability to work independently on complex problems in a team and fast-paced environment and demonstrated skill in performing tasks requiring organization and attention to detail. * Ability to support team initiatives, demonstrate respect for team members, and seek team consensus. The Ideal Seasonal Forecast System Developer will have the following: * Deep knowledge of ensemble strategy, reanalysis/reforecasting, and IC uncertainty. * Familiarity with operational NWP and/or an operational forecasting environment and experience with advanced technical workflows. * Experience with UFS applications and UFS infrastructure and knowledge of earth system modeling. * Familiarity with agile software development practices. ## Description Duties of the Seasonal Forecast System Developer will include the following: The candidate will perform job duties in a high-quality, independent, and collaborative way, developing and applying innovative methods for the primary work areas. The candidate will work with OMD scientists and external collaborators to develop and evaluate the current and future operational seasonal and marine forecasting systems. The candidate will work with the UFS infrastructure in designing and evaluating the global coupled ensemble forecasting systems for long range forecasting. Detailed responsibilities will include at least two aspects or more of the following areas. * Development of global coupled Numerical Weather Prediction (NWP) models. * Development of marine (ocean and sea ice) forecast systems. * Development of subseasonal to seasonal probabilistic forecasting systems. * Development of AI/ML techniques for seasonal and marine forecasting systems. * Designing and conducting retrospective ensemble experiments and re-forecasts. * Exploring initial or analysis uncertainties, and/or model uncertainties (or model stochastic perturbations). * Analysis and evaluation of S2S probabilistic forecasts, and applications of multi-model ensembles and statistical post processing.