Full Job Description
You will work with the plasma physics development team for Semiverse Solutions in the Austin, Texas office. As a Staff Computational Software Software Development Engineer at Lam, you will be at the forefront of innovation by developing and enhancing VizGlow, Lam's plasma physics solver. Your role is pivotal in improving reactor hardware design, optimizing process recipes, and advancing the fundamental understanding of the physics within reactor chambers. By developing software tools that support these critical areas, you will directly impact Lam's ability to deliver cutting-edge solutions.
What you'll do
• Develop and enhance plasma physics code with a focus on non-equilibrium plasmas, electromagnetics, reactive flows, and surface chemistry.
• Implement and optimize algorithms in C++ for high-performance computing applications.
• Collaborate with physicists and engineers to integrate new models and features into the software.
• Validate the operation and functionality of the plasma physics code through rigorous testing and debugging.
• Author technical reports summarizing software performance, defects, and enhancement requests.
• Participate in planning and design discussions with the software development team.
• Maintain and improve the existing codebase to ensure reliability and performance.
Who we're looking for
Minimum Qualifications
• Master's degree with 8+ years' relevant experience or PhD in physics, plasmas, fluid mechanics, or a related field with 5+ years' relevant experience
• Experience developing flow solvers - pressure-based flow solver, multi-species reactive flows, turbulence modelling, radiation modelling, etc.
• Experience developing HPC codes using MPI, multithreading, OpenMP, etc.
• Proficiency in C++ software development.
Preferred qualifications
• Knowledge of non-equilibrium plasmas, electromagnetics, reactive flows, and surface chemistry.
• Experience developing large scale and/or commercial computational fluid dynamics (CFD) or multi-physics software packages such as COMSOL or Ansys Fluent.
• Experience with GPU acceleration using libraries such as CUDA, Kokkos, etc.
• Familiarity/awareness with physics-based machine learning (PINNS, Graph Neural Networks Neural Operators) optional but highly desirable
• For candidates with the appropriate experience (Director+), open to level considerations
