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$25K

$80.3K

$163.5K

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

As of Jun 4, 2026, the average yearly pay for senior high performance computing in the United States is $80,287.00, according to ZipRecruiter salary data. Most workers in this role earn between $41,500.00 and $103,000.00 per year, depending on experience, location, and employer.

What is the difference between Senior High Performance Computing vs High Performance Computing Engineer?

AspectSenior High Performance ComputingHigh Performance Computing Engineer
CredentialsBachelor's or Master's in Computer Science, often with certifications in HPCBachelor's or Master's in Computer Science or related field, with HPC experience
Work EnvironmentResearch labs, universities, large data centersData centers, research institutions, tech companies
Industry UsageAcademic, research, government projectsCommercial, research, technology sectors
Primary FocusLeadership in HPC projects, strategic planningDesigning, developing, optimizing HPC systems and applications

While both roles involve high-performance computing, Senior High Performance Computing professionals typically focus on leadership and strategic oversight, whereas High Performance Computing Engineers are more involved in technical development and system optimization. The roles often overlap in skills and environment but differ in scope and responsibilities.

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Senior High Performance Computing jobs near you
Infographic showing various Senior High Performance Computing job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 72% Full Time, and 27% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $80,287 per year, or $38.6 per hour.

High Performance Computing (HPC) Engineer

GenBio AI

Palo Alto, CA • On-site

Full-time

Posted 28 days ago

Job description

Job Summary:
GenBio AI is a newly established start-up headquartered in Silicon Valley, dedicated to transforming biology and medicine through Generative AI. The HPC Engineer will design, deploy, and maintain high-performance GPU clusters, implement distributed computing techniques, and collaborate with data scientists to enhance model development and deployment frameworks.
Responsibilities:
• 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.
Qualifications:
Required:
• 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)
Company:
GenBio AI creates AI-driven models to simulate and predict biological systems at multiple scales. Founded in 2024, the company is headquartered in Palo Alto, USA, with a team of 11-50 employees. The company is currently Early Stage.

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