NextSilicon is reimagining high-performance computing. Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. Our new software-defined hardware architecture enables HPC to fulfill its promise of breakthroughs in all fields of advanced research.
At NextSilicon, everything we do is guided by three core values:
Professionalism: We strive for exceptional results through professionalism and unwavering dedication to quality and performance.
Unity: Collaboration is key to success. That's why we foster a work environment where every employee can feel valued and heard.
Impact: We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide.
Join our customer success team as an HPC Customer Solutions Engineer. We are seeking a highly skilled, customer-centric individual to act as the primary link between our cutting-edge HPC technology and its end-users.
Operating at the forefront of computer science and scientific applications, you will be instrumental in enabling our customers to achieve peak performance in diverse fields such as graph algorithms, sparse computations, weather prediction, and more. As our platform evolves to incorporate next-generation capabilities (including AI/ML), you will be a key resource in supporting these emerging technologies. You will not only directly support users but actively drive the usability and feature development of our platform by collaborating closely with our product and software engineering teams. If you are passionate about science and technology, thrive in a customer-facing role, and want to shape the future of high-performance computing, we want to hear from you.
We currently have 2 open requisitions for this Customer Solutions Engineer role.
Location: Hybrid in either our Austin, TX or Minneapolis, MN offices preferred but Remote considered for exceptional candidates.
Provide hands-on support, assisting customers in resolving technical issues for scientific HPC applications
Identify, troubleshoot, and reproduce resolving client-facing bugs and runtime errors, ensuring timely resolution and customer satisfaction
Drive new software features through direct customer engagement, and resolve client-facing issues.
Maintain up-to-date knowledge of key software features and communicate effectively with customers to provide guidance and support
Act as a liaison between internal teams, end-users, and relevant stakeholders to advocate for and fulfill customer needs, translating technical requirements into software features
Contribute to the development and upkeep of user documentation and support knowledge base
Profile and identify bottlenecks in a diverse range of HPC applications across various architectures
Requirements
Master's or higher degree in computer science, engineering, physics, or a related field; candidates may be considered if they have relevant experience in lieu of an advanced degree
Proficiency in C, C++, and/or Fortran programming languages
Working understanding of standards-based parallel programming models and languages such as MPI, OpenMP, CUDA, or OpenACC
Familiarity with HPC software stack components including LLVM compilers and toolchains, libraries, schedulers, and job submission systems (e.g., SLURM or PBS)
Experience with Linux/Unix operating systems and command-line tools for system navigation, administration, and troubleshooting
Knowledge of performance optimization techniques for parallel computing, including code profiling, tuning, and scaling
Exceptional written communication skills with a proven ability to produce clear, concise, and technically accurate documentation
Strong verbal communication for effective customer engagement and internal advocacy
Results-driven and relentless, with a bias toward creative solutions and a no-excuses approach to delivering outcomes
Desirable technical skills
Familiarity with Python and its relevant data science libraries (e.g., NumPy, Pandas)
Familiarity with key AI/ML frameworks (e.g., PyTorch, TensorFlow, JAX, vLLM and llama.cpp) and/or working knowledge of optimizing, profiling and debugging AI/ML workloads on parallel architectures (e.g., GPU acceleration, distributed training)