Job Description:

Lynker Technologies Machine Learning Engineer US-MD-College Park Job ID:

2026-1612

Type:

Full-Time # of

Openings:

1 College Park, MD Overview Lynker is seeking a talented and experienced Machine Learning Engineer to support the Environmental Modeling Center (EMC) within the National Centers for Environmental Prediction (NCEP). The primary objective of this role is to assist in the development of ML based systems that predict the current weather conditions everywhere given sparse observation data (this process is known as Data Assimilation [DA]). . These systems will complement existing physics-based systems and be tested as independent prototypes, running alongside traditional DA workflows. The position is located at the NOAA Center for Weather and Climate Prediction (NCWCP) in College Park, MD. Responsibilities Duties of the Machine Learning Engineer will include the following: The Machine Learning Engineer will perform their job duties to a high standard, working both independently and collaboratively.

The core responsibility is to assist in the development, implementation, testing, and evaluation of an AI-based Real-Time Mesoscale Analysis (AI-RTMA) system in support of NOAA's National Blend of Models (NBM). The AI-RTMA system will generate high spatial and temporal resolution analyses of meteorological variables to reduce biases in the NBM fields.. Because these fields serve as the foundation for gridded forecasts issued by the National Weather Service, this system will directly contribute to improved forecast quality. The successful Machine Learning Engineer will work on the following scientific and engineering tasks: Conduct a comprehensive review of state-of-the-art AI-based data assimilation and end-to-end weather forecasting methodologies, systems, and frameworks. Communicate findings with EMC scientists and external partners to inform the development of a scientifically robust and efficient AI-RTMA approach. Collaborate with NOAA's NBM team and key stakeholders to define product requirements for AI-RTMA, including domain configuration, grid structure, output variables, spatial and temporal resolution, and data formats suitable for operational evaluation and transition. Design, implement, and maintain robust data pipelines to support AI-RTMA training, validation, testing, and evaluation. This includes collecting, formatting, quality-controlling, and integrating diverse observational datasets (e.g., conventional observations, satellite, radar, and other sources), as well as preparing model inputs, targets, metadata, and training/validation splits. Develop, train, rigorously test, and deploy a fully functional AI-RTMA system based on selected AI frameworks or architectures. Implement cross-validation and other evaluation methodologies to quantify model performance and reliability during inference. Qualifications The Machine Learning Engineer selected should have the following: Experience developing, training and deploying AI-based systems applied to geophysical systems. Experience with common AI frameworks such as PyTorch, TensorFlow. Experience working with earth observation data, including conventional observations, satellite, radar. Excellent Python programming skills. Practical experience utilizing High Performance Computers (HPCs) and GPUs. Proven experience working in a UNIX environment with advanced scripting languages. Good communication skills, both oral and written, in English. The Ideal Machine Learning Engineer will have the following: In-depth knowledge of data assimilation techniques (observation forward modeling, quality control, variational-based and/or ensemble methods). Strong foundation in the physical, statistical and mathematical basis of geophysical modeling (atmospheric and/or environmental). Experience with cloud platforms and use of IDEs for development. Experience with cloud-native data formats such as Zarr, Parquet. Experience with compiled languages. Comfort using agentic AI tools to accelerate development. Experience executing numerical models on HPC platforms using parallelization frameworks and job scheduling systems. Familiarity with coupled earth system models. Knowledge of modern software engineering practices (requirements gathering, design, prototyping, version control, integration, testing, and documentation). Prior experience in model testing, evaluation, or knowledge of verification principles. 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 .

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PI285882541

Salary:

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