Hydrologic Modeler: ML & Python for Large Watersheds

Lynker Technologies; Boulder (CO); On-site; USD 120,000 - 160,000; Full time

Job summary

Lynker Technologies is seeking a Hydrologic Modeler to advance weather and water modeling. The candidate will train and run large-scale hydrologic models across regional and continental domains, calibrate and evaluate models, and apply ML/DL methods to improve predictions.

Strong Python and PyTorch expertise, plus experience with model forcing generation, Linux, and Git, are required. Colorado-based on-site collaboration is expected.

Qualifications

• Master's in hydrology, water resources, engineering, hydrologic sciences, applied mathematics, computer science, or related field; or Bachelor's plus 2 years training and running hydrologic, physics-based, and/or machine-learning models.

Responsibilities

• Train, execute, and synthesize large-scale physics-based and ML hydrologic models across regional to continental domains.
• Analyze model results to characterize performance, flow signatures, and hydrologic behavior, and communicate findings to the modeling team.
• Calibrate, benchmark, and validate models, quantify uncertainty, and diagnose where and why predictions succeed or fail.
• Generate and quality-check model forcings and manage training datasets that drive these models.
• Apply deep-learning approaches to hydrologic prediction, including NeuralHydrology and dHBV.
• Help develop, test, and improve hydrologic and hydraulic routing tools, primarily in Python.
• Share well-documented, reproducible code, methods, and results with the Modeling, Science, and Platform teams.

Skills

Python

PyTorch

Hydrology

Git

Linux

Machine learning

Education

Master's in hydrology or related field

Bachelor's + 2 years related experience

Tools

NeuralHydrology

dHBV

Job description

Lynker Technologies is seeking a Hydrologic Modeler to advance weather and water modeling. The candidate will train and run large-scale hydrologic models across regional and continental domains, calibrate and evaluate models, and apply ML/DL methods to improve predictions.

Strong Python and PyTorch expertise, plus experience with model forcing generation, Linux, and Git, are required. Colorado-based on-site collaboration is expected.