Hydrologic Modeler

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

Hydrologic Modeler

US-CO-Boulder

Boulder, CO

Job ID: 2026-1645

Type: Full-Time

# of Openings: 1

Overview

Lynker Corporation is a leading provider of innovative solutions in weather and climate science. With a commitment to excellence and a passion for innovation, Lynker leverages cutting-edge technologies and scientific expertise to support the creation and delivery of improved operational weather forecasts.

As part of our ongoing growth and expansion, we are seeking a dynamic and experienced Hydrologic Modeler to join our growing team.

Responsibilities

Duties of the Hydrologic Modeler will include the following:

• Model training & execution: train, execute, and synthesize large-scale physics-based and machine-learning hydrologic models across regional to continental domains.
• Synthesis & interpretation: analyze model results to characterize performance, flow signatures, and hydrologic behavior, and communicate findings to the modeling team.
• Calibration & evaluation: calibrate, benchmark, and validate models, quantify uncertainty, and diagnose where and why predictions succeed or fail.
• Forcing & training: generate and quality-check model forcings and manage the training processes and datasets that drive these models.
• ML/AI methods: apply deep-learning approaches to hydrologic prediction, including tools such as NeuralHydrology and dHBV.
• Routing tools: help develop, test, and improve hydrologic and hydraulic routing tools, primarily in Python.
• Collaboration: contribute to the Modeling, Science, and Platform teams by sharing well-documented, reproducible code, methods, and results.

Qualifications

The Hydrologic Modeler selected should have the following:

• Master's in hydrology, water resources, engineering, hydrologic sciences, applied mathematics, computer science, or a related field; or a Bachelor's plus 2 years training and running hydrologic, physics-based, and/or machine-learning models; or equivalent experience.
• Background in hydrology, ideally including ecological or environmental flows, with an understanding of how water moves through watersheds and river systems.
• Strong Python skills, and experience with a deep-learning framework such as PyTorch.
• Understanding of model forcing generation and training workflows for large-scale models.
• Comfort working with large gridded and time-series datasets, Git, and Linux.

The Ideal Hydrologic Modeler will have the following:

• Experience with machine-learning hydrologic modeling tools such as NeuralHydrology and dHBV, or comparable physics-informed and differentiable modeling approaches.
• Experience developing or applying hydrologic and hydraulic routing methods.
• Familiarity with the Next Generation Water Resource Modeling Framework.
• Experience with high-performance or parallel computing for large-scale model runs.
• Track record of scientific writing, publication, or open-source contribution.
• Colorado Front Range presence for periodic in-person collaboration.

About Lynker

Lynker is a growing, employee owned business, specializing in professional, scientific and technical services. Our continually expanding team combines scientific expertise with mature, results-driven processes and tools to achieve technically sound, cost effective solutions in hydrology/water sciences, geospatial analysis, information technology, resource management, conservation, and management and business process improvement.

We focus on putting the right people in the right place to be effective. And having the right people is critical for success. Our streamlined organization enables and empowers our talented professionals to tackle our customers' scientific and technical priorities creatively and effectively.

Lynker offers a team-oriented work environment, and the opportunity to work in a culture of exceptionally skilled professionals who embrace sound science and creative solutions. Lynker's benefits include the following:

• Personalized career growth plans for every employee

Lynker is an E-Verify employer.

Lynker is an equal opportunity employer and makes all employment decisions based on merit, qualifications, and business needs. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other legally protected status under federal, state, or local laws