Staff Software Computational Engineer

You will work with the plasma physics development team forSemiverseSolutions 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 enhancingVizGlow, Lam's plasma physics solver. Your role is pivotal in improving reactor hardware design,optimizingprocess recipes, and advancing the fundamental understanding ofthe physicswithin reactor chambers. By developing software tools that support these critical areas, you will directlyimpactLam's ability to delivercutting-edgesolutions.

• 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.

Minimum Qualifications

• Master'sdegree with8+years' relevant experienceor PhDin physics, plasmas, fluid mechanics, orarelated fieldwith 5+ years' relevant experience

• Experience developing flow solvers -pressure-basedflow solver, multi-species reactive flows, turbulence modelling, radiation modelling, etc.

• Experience developing HPC codes using MPI, multithreading, OpenMP, etc.

• Proficiencyin C++ software development.

• Knowledge of non-equilibrium plasmas, electromagnetics, reactive flows, and surface chemistry.

• Experiencedevelopinglarge scale and/orcommercialcomputational fluid dynamics (CFD) or multi-physics software packages such as COMSOLor AnsysFluent.

• Experience with GPU accelerationusinglibraries such asCUDA,Kokkos, etc.

• Familiarity/awarenesswith physics-based machine learning (PINNS, Graph Neural NetworksNeural Operators) optional but highly desirable
• For candidates with the appropriate experience (Director+), open to level considerations