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Entry Level High Performance Computing Engineer Jobs in California

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Entry Level High Performance Computing Engineer information

What is the average salary for an HPC engineer in the US?

The average salary for an entry-level high performance computing (HPC) engineer in the US typically ranges from $70,000 to $90,000 annually, depending on experience, location, and specific technical skills such as parallel programming and cluster management. Salaries tend to increase with expertise in areas like GPU computing, Linux systems, and performance optimization.

What is an Entry Level High Performance Computing Engineer?

An Entry Level High Performance Computing (HPC) Engineer is a professional who assists in designing, building, and maintaining high-speed computing systems used for complex computations and large-scale data analysis. They typically work with supercomputers or computer clusters in fields like scientific research, finance, or engineering. Responsibilities often include configuring hardware, optimizing software, and troubleshooting system issues, usually under the guidance of more experienced engineers. Entry-level engineers may also help monitor system performance and support users in running high-performance applications.

What engineers make $500,000?

High-level engineers in specialized fields such as software engineering, data engineering, or systems engineering can reach salaries of $500,000 or more, especially with extensive experience, advanced skills, and leadership roles. These roles often require expertise in high-demand technologies, certifications, and working in competitive industries or companies with lucrative compensation packages.

What engineers make $300,000 a year?

High-performance computing engineers with extensive experience, advanced skills in parallel programming, and expertise in hardware and software optimization can reach annual salaries of $300,000 or more, especially in senior or specialized roles. Such compensation often reflects leadership responsibilities, advanced certifications, or working in high-demand industries like technology or finance.

Can computer engineers make $500,000?

Entry-level high performance computing engineers typically do not earn $500,000 annually; such salaries are usually associated with senior roles, specialized skills, or leadership positions in the field. High salaries in this area often require extensive experience, advanced certifications, or working in high-demand industries or companies. Most entry-level positions start with lower compensation, but salaries can increase significantly with experience and expertise in areas like parallel computing, GPU programming, or data center management.

What are some common challenges faced by entry level High Performance Computing (HPC) engineers, and how can new hires successfully navigate them?

Entry level HPC engineers often encounter challenges such as working with complex parallel computing architectures, optimizing code for performance, and troubleshooting across large-scale, distributed systems. New hires may also need to quickly learn job-specific tools and adapt to rapidly evolving hardware and software environments. To navigate these challenges, it’s important to proactively seek mentorship, participate in team code reviews, and continuously build your skills through hands-on experience and training opportunities. Open communication and collaboration with experienced team members also play a key role in overcoming technical hurdles and growing within the HPC field.

What is the difference between Entry Level High Performance Computing Engineer vs Entry Level Data Scientist?

AspectEntry Level High Performance Computing EngineerEntry Level Data Scientist
Required CredentialsBachelor's in Computer Science, Engineering, or related field; knowledge of parallel computingBachelor's in Data Science, Statistics, or related; programming skills in Python/R
Work EnvironmentResearch labs, tech companies, supercomputing centersBusiness, tech firms, research institutions
Industry UsageHigh-performance computing, scientific research, simulationsData analysis, machine learning, predictive modeling

Entry Level High Performance Computing Engineers focus on developing and optimizing computational systems for scientific and technical applications, while Entry Level Data Scientists analyze data to extract insights. Both roles require programming skills and a strong technical background, but they serve different industry needs and environments.

What are the key skills and qualifications needed to thrive as an Entry Level High Performance Computing (HPC) Engineer, and why are they important?

To thrive as an Entry Level High Performance Computing Engineer, you typically need a solid background in computer science or engineering, familiarity with parallel computing concepts, and proficiency in programming languages like C/C++ or Python. Experience with Linux environments, HPC cluster management tools, and knowledge of batch schedulers or MPI/OpenMP are often required. Strong problem-solving abilities, teamwork, and effective communication help you excel in collaborating with researchers and technical teams. These skills ensure efficient support and optimization of complex computing systems critical for scientific and technical advancements.
What are the most commonly searched types of High Performance Computing Engineer jobs in California? The most popular types of High Performance Computing Engineer jobs in California are:
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Entry Level High Performance Computing Engineer jobs near you
Infographic showing various Entry Level High Performance Computing Engineer job openings in California as of June 2026, with employment types broken down into 25% Internship, 50% Full Time, and 25% Contract. Highlights an 100% In-person job distribution.

High Performance Computing (HPC) Engineer

GenBio AI

Palo Alto, CA • On-site

$170K - $260K/yr

Full-time

Posted 6 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)
$170,000 - $260,000 a year
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 and identifying potential inconsistencies or verification signals in application materials based on available information. 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.
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