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Scientific Developer for Model Optimization

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 Scientific Developer for Model Optimization to join our growing team. The ideal Scientific Software Developer for MPAS Infrastructure will be local to the DC Metro area, and be able to work a hybrid schedule, working 2 days per week in office out of the College Park, MD location, and working from home the remaining 3 days of the week. Responsibilities Duties of the Scientific Developer for Model Optimization will include the following:

• Identify and resolve performance bottlenecks in the UFS Weather Model with the newly integrated MPAS atmospheric dynamic core and coupled components.
• Identify opportunities for model performance improvement, including through code optimization, build configuration, and heterogeneous and portable computing, especially with CPU and GPU.
• Implement code run-time profiling for large parallel applications, including profiling of

CPU & GPU

performance, memory usage, cache usage, and disk I/O.

• Develop configurable, reproducible frameworks/workflows for doing so.
• Enable the integration of optimized, GPU/accelerator-enabled and profiling-enabled components into the UFS build system, including configuration of compilers, packages, and profiling/instrumentation.
• Identify available computing hardware resources and port and test accelerated code on appropriate systems, ensuring robust portability and reproducibility within acceptable tolerance.
• Investigate bit-for-bit reproducibility in the context of CPU and GPU optimizations, and propose and implement appropriate reproducibility tests.
• Contribute to strategic, technical, and other documentation regarding profiling/instrumentation, optimization, and GPU/portable programming .
• Collaborate with other teams to support models, forecast workflows, and other functions through the delivery of optimized code and profiling/instrumentation frameworks. Provide technical guidance and mentoring to colleagues on profiling/instrumentation, optimization, and GPU/portable code development.
• Explore and validate the use of Generative AI (GenAI)/Large Language Models (LLMs) to improve the quality of code and the efficiency of code development process.
  Qualifications
  The Scientific Developer for Model Optimization selected should have the following:
  
• Bachelor's or Master's degree in meteorology, atmospheric science, physical sciences, mathematics, computer science, software engineering, scientific computing, or a closely related field with demonstrated numerical modeling or closely related scientific computing experience.
  
• Demonstrated experience in high-performance computing (HPC), software optimization, and building, running, debugging, and profiling large-scale parallel applications.
  
• Proficiency in C, C++, and/or Fortran, as well as MPI.
  
• Familiarity with GPU/heterogenous programming and/or portability frameworks (e.g., CUDA, OpenACC, OpenMP target offloading, Kokkos, RAJA, PyTorch, JAX).
  
• Familiarity with profiling concepts and/or tools (e.g., gprof, TAU, Scalasca)
  
• Experience with Linux/Unix shell scripting and automation.
  
• Demonstrated experience with build systems (e.g., CMake, Meson, autotools, make) is strongly preferred, however, strong proficiency in configurable scripting and automation is acceptable.
  
• Demonstrated proficiency with Git or similar tools for code collaboration and management.
  
• Demonstrated ability to apply the concepts of software engineering and best practices.
  The Ideal Scientific Developer for Model Optimization will have the following:
  
• Ph.D. in a quantitative field with research computing experience.
  
• Direct experience with the Unified Forecast System (UFS), MPAS, or other atmospheric modeling systems. Familiarity with NWP forecast workflows is beneficial.
  
• Experience with workflow (ecFlow, Rocoto, Cylc) and package (Spack, pip) management tools in HPC environments.
  
• Familiarity with leveraging GenAI/LLM tools to optimize and assist software development workflows.
  
• Familiarity with continuous integration (CI) workflows and frameworks.

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