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Entry Level High Performance Computing Engineer Jobs in California

AMAX

Jr System Engineer

Fremont, CA · On-site

... of high-performance computing systems and server platforms. The ideal candidate has strong ... Collaborate with engineering teams to define system specifications and evaluate the interface ...

AMAX

Jr System Engineer

Fremont, CA

... of high-performance computing systems and server platforms. The ideal candidate has strong ... Collaborate with engineering teams to define system specifications and evaluate the interface ...

AMAX

Jr System Engineer

Fremont, CA · On-site

... of high-performance computing systems and server platforms. The ideal candidate has strong ... Collaborate with engineering teams to define system specifications and evaluate the interface ...

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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:
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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 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.
Deep Learning Kernel Software Performance Architect - New College Grad 2026

Deep Learning Kernel Software Performance Architect - New College Grad 2026

NVIDIA

Santa Clara, CA • On-site

$196.10K/yr

Full-time

Posted 15 days ago

Job description

Job Summary:
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. They are looking for a Performance Architect for Deep Learning Software to develop processor and system architectures that accelerate machine learning and data analytics applications.
Responsibilities:
• Validate and analyze performance of GPU-accelerated system and software architectures that advance the frontier of deep learning performance.
• Debug deep learning and data analytics software to identify root causes of performance bottlenecks.
• Develop scripts and tools to analyze, visualize, and debug software using analytical models, simulators, and test suites
• Collaborate across NVIDIA teams:
• Work with the CUDA and AI Compiler teams to pinpoint and resolve performance issues
• Engage AI/ML training and inference performance teams to identify and optimize critical deep learning layers
• Collaborate with hardware architecture performance teams to define expectations for emerging deep learning hardware features
Qualifications:
Required:
• Master's or PhD in Computer Science, Electrical Engineering or Computer Engineering, or equivalent experience.
• Proven expertise in software design, including debugging, performance analysis, and test development
• Hands-on experience with practical parallel programming, even if it’s not on GPUs.
• Strong understanding of computer architecture, with practical experience on performance debugging.
• Ability to identify bottlenecks, optimize resource utilization, and enhance system throughput
• Fluency in programming languages such as Python, C, C++.
Preferred:
• Strong foundation in machine learning and deep learning fundamentals to complement your expertise in computer architecture.
• A strong background in high performance power efficient designs, energy efficient high-performance computing, performance analysis and profiling to identify performance bottlenecks.
• Experience and familiarity with GPU computing and parallel programming models.
• Work experience with analytical performance modeling, profiling, and analysis
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.
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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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