Hybrid Physical Scientist: Seasonal Forecast System Developer

Lynker; College Park (MD); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Comprehensive healthcare

PTO and paid holidays

401(k) with company matching

ESOP

Tuition assistance

Spot bonuses

Annual recognition Awards

Employee referrals

Learning Management System

Career growth plans

Job summary

Lynker seeks a Seasonal Forecast System Developer to advance marine and seasonal forecasting within the UFS/SFS framework, working with OMD at NCWCP in Riverdale Park, MD. The role blends research, software development, and collaboration with external partners.

Ideal candidates have expertise in AI/ML methods for geophysical systems, and proficiency in FORTRAN/Python, with HPC experience and modern software practices. Hybrid schedule offered.

Qualifications

• Master or PhD in atmospheric sciences or related physical science with 1–2 years of related experience.
• Experience in global coupled ensemble forecast systems, sub-seasonal to seasonal prediction, AI/ML models.
• Proficiency in FORTRAN and Python; HPC scripting; Git/GitHub version control.

Responsibilities

• Develop and evaluate global coupled ensemble forecasting systems for long-range forecasting.
• Design and evaluate SFS-based marine and seasonal forecasting components.
• Conduct retrospective experiments and analyze probabilistic forecasts.
• Collaborate with scientists and external partners to advance operational systems.

Skills

S2S prediction

AI/ML methods

FORTRAN

Python

HPC

Git/GitHub

Communication

Independent work

Agile practices

UFS applications

Education

Master or PhD in atmospheric sciences or related physical science

Tools

UFS infrastructure

Job description

Lynker seeks a Seasonal Forecast System Developer to advance marine and seasonal forecasting within the UFS/SFS framework, working with OMD at NCWCP in Riverdale Park, MD. The role blends research, software development, and collaboration with external partners.

Ideal candidates have expertise in AI/ML methods for geophysical systems, and proficiency in FORTRAN/Python, with HPC experience and modern software practices. Hybrid schedule offered.