Scientific Developer for Model Optimization

Axiom is seeking a Scientific Developer 

Responsibilities:

• 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:

• 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.

Preferred Qualifications:

• 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.