Machine Learning Engineer

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 (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.

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 and directly contribute to improved forecast quality.

• 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.

• Experience developing, training, and deploying AI-based systems applied to geophysical systems.
• Experience with common AI frameworks such as PyTorch and Tensor Flow.
• Experience working with earth observation data, including conventional observations, satellite, and 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.

• 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 and 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.

Lynker is a growing, employee-owned business specializing in professional, scientific, and technical services. Our 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,…