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High Performance Computing Hpc information

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$32.1K

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$110.5K

How much do high performance computing hpc jobs pay per year?

As of May 29, 2026, the average yearly pay for high performance computing hpc in California is $67,355.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,300.00 and $81,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a High Performance Computing (HPC) specialist, and why are they important?

To thrive as a High Performance Computing (HPC) specialist, you need a solid background in computer science or engineering, strong programming skills (especially in languages like C, C++, or Python), and expertise in parallel computing and Linux systems. Familiarity with cluster management tools, job schedulers (e.g., SLURM or PBS), and experience with HPC libraries and accelerators such as MPI, OpenMP, and GPU programming are typically required. Excellent problem-solving abilities, teamwork, and effective communication skills help you collaborate with researchers and resolve complex technical challenges. These competencies are vital for optimizing computational workflows, maintaining robust systems, and enabling advanced scientific or industrial research.

What are some common challenges faced by professionals working in High Performance Computing (HPC) environments?

Professionals in HPC roles often encounter challenges such as optimizing code for parallel processing, managing complex and rapidly evolving hardware architectures, and troubleshooting large-scale distributed systems. Collaborating closely with researchers and domain experts is also essential to ensure that computational resources are used efficiently and effectively. Keeping up with advances in both hardware and software, as well as balancing multiple projects with tight deadlines, are typical aspects of the HPC work environment.

What is High Performance Computing (HPC)?

High Performance Computing (HPC) refers to the use of supercomputers and parallel processing techniques to solve complex computational problems quickly and efficiently. HPC systems combine the power of multiple processors to perform billions or even trillions of calculations per second, making them essential for scientific research, engineering simulations, data analytics, and other demanding tasks. These systems are used in fields such as weather forecasting, molecular modeling, financial modeling, and artificial intelligence. By leveraging HPC, organizations can tackle problems that are too large or complex for standard computers.

What is the difference between High Performance Computing Hpc vs Data Scientist?

AspectHigh Performance Computing (HPC)Data Scientist
Required credentialsDegree in Computer Science, Engineering, or related fields; often certifications in parallel computing or HPC systemsDegree in Data Science, Statistics, Computer Science, or related fields; certifications in data analysis or machine learning
Work environmentSupercomputing centers, research labs, large enterprises with high computational needsTech companies, finance, healthcare, research institutions, often in office or remote settings
Industry usageScientific research, simulations, modeling, large-scale data processingData analysis, predictive modeling, machine learning, business insights

While both roles involve working with large datasets and complex computations, HPC specialists focus on designing and maintaining high-performance computing systems for scientific and engineering tasks. Data scientists analyze data to extract insights and build models. The roles often overlap in data processing but differ in technical focus and environment.

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High Performance Computing Hpc jobs near you

High Performance Computing (HPC) Engineer

GenBio AI

Palo Alto, CA

Full-time

Posted 23 days ago

Job description

Headquartered in Silicon Valley, we are a newly established start-up, where a collective of visionary scientists, engineers, and entrepreneurs are dedicated to transforming the landscape of biology and medicine through the power of Generative AI. Our team comprises leading minds and innovators in AI and Biological Science, pushing the boundaries of what is possible. We are dreamers who reimagine a new paradigm for biology and medicine.
 
We are committed to decoding biology holistically and enabling the next generation of life-transforming solutions. As the first mover in pan-modal Large Biological Models (LBM), we are pioneering a new era of biomedicine, with our LBM training leading to ground-breaking advancements and a transformative approach to healthcare. Our exceptionally strong R&D team and leadership in LLM and generative AI position us at the forefront of this revolutionary field. With headquarters in Silicon Valley, California, and a branch office in Paris, we are poised to make a global impact. Join us as we embark on this journey to redefine the future of biology and medicine through the transformative power of Generative AI.
Job Description
  • GPU Cluster Management: Design, deploy, and maintain high-performance GPU clusters, ensuring their stability, reliability, and scalability. Monitor and manage cluster resources to maximize utilization and efficiency.
  • Distributed/Parallel Training: Implement distributed computing techniques to enable parallel training of large deep learning models across multiple GPUs and nodes. Optimize data distribution and synchronization to achieve faster convergence and reduced training times.
  • Performance Optimization: Fine-tune GPU clusters and deep learning frameworks to achieve optimal performance for specific workloads. Identify and resolve performance bottlenecks through profiling and system analysis.
  • Deep Learning Framework Integration: Collaborate with data scientists and machine learning engineers to integrate distributed training capabilities into GenBio AI’s model development and deployment frameworks. 
  • Scalability and Resource Management: Ensure that the GPU clusters can scale effectively to handle increasing computational demands. Develop resource management strategies to prioritize and allocate computing resources based on project requirements. 
  • Troubleshooting and Support: Troubleshoot and resolve issues related to GPU clusters, distributed training, and performance anomalies. Provide technical support to users and resolve technical challenges efficiently.
  • Documentation: Create and maintain documentation related to GPU cluster configuration, distributed training workflows, and best practices to ensure knowledge sharing and seamless onboarding of new team members.
Job Requirements:
  • Master’s or Ph.D. degree in computer science, or a related field with a focus on High-Performance Computing, Distributed Systems, or Deep Learning.
  • 2+ years proven experience in managing GPU clusters, including installation, configuration, and optimization.
  • Strong expertise in distributed deep learning and parallel training techniques.
  • Proficiency in popular deep learning frameworks like PyTorch, Megatron-LM, DeepSpeed, etc.
  • Programming skills in Python and experience with GPU-accelerated libraries (e.g., CUDA, cuDNN).
  • Knowledge of performance profiling and optimization tools for HPC and deep learning.
  • Familiarity with resource management and scheduling systems (e.g., SLURM, Kubernetes)
  • Strong background in distributed systems, cloud computing (AWS, GCP), and containerization (Docker, Kubernetes)
Join us as we embark on this journey to redefine the future of biology and medicine.
We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security’s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.