Senior Scientific Software Engineer

Xcimer Energy leverages decades of research on Inertial Fusion Energy (IFE) combined with groundbreaking new laser architecture. Our mission is to deploy fusion power plants to meet global decarbonization goals as fast as possible. Xcimer has assembled a team of leaders in tough tech, fusion science, and manufacturing with a track record of rapid execution. Supported by leading investors, Xcimer is uniquely positioned to deliver limitless, clean, fusion power to combat climate change. Join us in powering a better world with inertial fusion!

This is a full-time, onsite role based at our headquarters in Denver, CO.

As a Senior Scientific Software Engineer, you lead the design and development of software systems that support scientific analysis, visualization, machine learning, and internal applications. You will be part of a high-performing software team working closely with simulation and experimental groups to turn data, models, and control systems into reliable, scalable tools that accelerate R&D.

This is a senior, hands-on role with ownership over technical direction and system architecture. A physics background is not required, but you should be comfortable operating in data-driven, research-oriented environments and translating evolving requirements into production-quality software. We are looking for our engineers and scientists to apply their technical expertise, problem solving skills, and dedication to quality to positively impact the future of energy!

Responsibilities

Software Architecture & Design

• 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.

Data Pipelines & Analysis

• 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.

Performance & Integration

• 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.

Technical Leadership

• 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.

Qualification

• 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.

Desired

• 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).