About The Role

Architect, develop, and maintain Python-based systems for data analysis, visualization, and internal workflows.
Lead technical design from early research prototypes through production deployment.
Partner with researchers and engineers to translate experimental and modeling requirements into scalable software solutions.
Establish best practices for testing, documentation, CI/CD, and deployment in containerized environments.

Design and support data processing pipelines, including large-scale and streaming datasets.
Build interactive, multi-user tools for exploratory data analysis and visualization.
Contribute to machine learning systems, including model integration, training pipelines, and support for real-time or adaptive control workflows.

Integrate Python applications with high-performance computing (HPC) or legacy codes (C++/Fortran) and distributed compute systems.
Optimize performance-critical components using parallel, GPU-accelerated, or distributed computing techniques as appropriate.

Mentor engineers and provide technical leadership across projects.
Serve as a senior member of the software team, setting direction and raising the bar for technical quality.

Education: Bachelor's degree in Computer Science, Engineering, Physics, Applied Mathematics, or related field; advanced degree preferred.
Experience: 7+ years of professional software development experience in technical, data-intensive, or research-driven environments.
Expert-level Python skills and strong experience with numerical and data libraries (NumPy, SciPy, Pandas, Matplotlib, Scikit-Learn).
Demonstrated ability to design and own complex software systems under ambiguous or evolving requirements.
Experience collaborating closely with non-software domain experts and supporting experimental or simulation workflows.
Must be a U.S. citizen or national, U.S. permanent resident (current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Experience working with 3D geometry or CAD data (STEP, IGES, STL, BREP) and related libraries (OpenCASCADE, VTK, PyVista).
Familiarity with structured scientific data formats (HDF5, NetCDF, VTK, custom binary formats).
Experience interfacing Python with C/C++ or Fortran (pybind11, Cython, f2py, nanobind).
Strong background in parallel, GPU, or distributed computing (MPI, CUDA, OpenACC).
Experience with machine learning systems in production environments.
Background supporting real-time systems, feedback loops, or adaptive control applications.
Experience with containerization and orchestration (Docker, Apptainer, Kubernetes) as well as HPC environments and job schedulers (SLURM).