ZipRecruiter

Log In

2

Entry Level High Performance Computing Engineer Jobs in California

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... high-performance computing • Familiarity with profiling tools, performance debugging, tracing ... both engineers and customers Preferred : • Experience with CUDA, Triton, Pallas, ROCm, XLA, or ...

next page

Showing results 1-20

People also search for

All JobsHigh Performance Computing Engineer JobsEntry Level High Performance Computing Engineer JobsEntry Level High Performance Computing Engineer Jobs in California

Entry Level High Performance Computing Engineer information

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 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 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 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 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:
What are popular job titles related to Entry Level High Performance Computing Engineer jobs in California? For Entry Level High Performance Computing Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Entry Level High Performance Computing Engineer jobs in California look for? The top searched job categories for Entry Level High Performance Computing Engineer jobs in California are:
What cities in California are hiring for Entry Level High Performance Computing Engineer jobs? Cities in California with the most Entry Level High Performance Computing Engineer job openings:
Entry Level High Performance Computing Engineer jobs near you
Infographic showing various Entry Level High Performance Computing Engineer job openings in California as of May 2026, with employment types broken down into 1% As Needed, 93% Full Time, 1% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 97% Physical, 2% Hybrid, and 1% Remote job distribution.

High Performance Computing (HPC) Engineer

GenBio AI

Palo Alto, CA • On-site

Full-time

Posted 24 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.

Apply