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Hpc Performance Engineer Jobs in California (NOW HIRING)

Apple

SW Optimization Engineer AI/ML

Cupertino, CA · On-site

$172K/yr

Experience with AI/ML, graphics, or HPC performance benchmarks and workloads. Preferred Qualifications M.S. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a closely ...

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Hpc Performance Engineer information

What is an HPC performance engineer?

HPC Performance Engineers are specialists who focus on optimizing the performance of high-performance computing (HPC) systems and applications. They analyze system bottlenecks, tune software and hardware configurations, and work with researchers and developers to ensure applications run efficiently on supercomputers or large computing clusters. Their work is essential for maximizing computational resources and improving the speed and scalability of scientific, engineering, or data-intensive workloads.

What are the key skills and qualifications needed to thrive as an HPC performance engineer?

To thrive as an HPC Performance Engineer, you need a strong background in computer science or engineering, with expertise in parallel programming, high-performance computing architectures, and performance analysis. Familiarity with tools like MPI, OpenMP, profiling software (e.g., Intel VTune, GNU gprof), and experience with job schedulers and Linux systems are essential. Analytical thinking, problem-solving, and effective communication are crucial soft skills for identifying bottlenecks and collaborating with multidisciplinary teams. These skills are vital for optimizing computational workflows, maximizing resource utilization, and driving efficiency in complex HPC environments.

What are the typical challenges HPC performance engineers face when optimizing large-scale computational workloads?

HPC Performance Engineers often encounter challenges such as identifying bottlenecks in parallel code, managing resource contention, and optimizing data movement across distributed systems. They must balance maximizing throughput with minimizing latency, all while ensuring applications scale efficiently as cluster sizes grow. Collaboration with software developers, system administrators, and research teams is common to align application requirements with hardware capabilities and to implement effective performance improvements.

What is the difference between Hpc Performance Engineer vs Hpc System Administrator?

AspectHpc Performance EngineerHpc System Administrator
Primary FocusOptimizing HPC system performance and efficiencyManaging and maintaining HPC infrastructure
Skills & CertificationsPerformance tuning, parallel computing, Linux, scriptingSystem setup, network management, user support
Work EnvironmentResearch labs, data centers, high-performance computing facilitiesData centers, IT departments, research institutions
Common TasksPerformance analysis, bottleneck resolution, code optimizationSystem installation, user account management, hardware troubleshooting

The Hpc Performance Engineer focuses on enhancing system performance and efficiency, often working on optimization and tuning. In contrast, the Hpc System Administrator manages the day-to-day operation and maintenance of HPC systems. Both roles are essential in high-performance computing environments but serve different core functions.

What cities in California are hiring for Hpc Performance Engineer jobs?

Cities in California with the most Hpc Performance Engineer job openings:

Hpc Performance Engineer jobs near you

Research Engineer - AI Performance & Kernel Optimization

San Francisco, CA • On-site

Zyphra Technologies Inc
Computer and Peripheral Equipment Manufacturing • 1 - 10 employees
Learn more about Zyphra Technologies Incexternal

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 27 days ago

Key responsibilities

  • Improve and optimize the performance of large-scale language model training and inference stacks.

  • Identify bottlenecks and design and implement highly optimized kernels across various hardware platforms.

  • Profile and eliminate bottlenecks in memory movement, communication, scheduling, and compute utilization.

Job description

Zyphra is an artificial intelligence company based in San Francisco, California.
The Role:
As a Research Engineer - AI Performance & Kernel Optimization, you will improve and optimize the performance of our large-scale language model training and inference stacks. You will work closely with our pretraining and inference teams to identify bottlenecks, design and implement highly optimized kernels, and push the limits of throughput, latency, and hardware utilization across a range of accelerator platforms. This role is suited for someone who enjoys deep systems work, cares about performance at every level of the stack, and is excited to translate low-level optimizations into meaningful gains for frontier-scale AI systems.
You'll Work Across:
  • Kernel development and optimization for large-scale ML workloads, using any level of the stack from PTX/assembly to CUDA, HIP, Triton, or other GPU DSLs
  • Performance tuning for training and inference stacks across GPUs and other accelerators
  • Profiling and eliminating bottlenecks in memory movement, communication, scheduling, and compute utilization
  • Optimizing distributed training and inference systems for large MoE models, including large-scale model parallelism
  • Portability and optimization across non-NVIDIA hardware, with special interest in AMD hardware such as the MI300x and MI355x
  • Collaboration with research and infrastructure teams to turn systems improvements into real-world model training and inference gains

What We're Looking For / Requirements:
  • Strong engineering aptitude for building reliable, high-performance systems
  • Excellent low-level performance intuition and the ability to reason about hardware-software interactions
  • Are excited to rapidly learn new systems, tools, and hardware environments
  • Excellent communication and collaboration skills, with the ability to work effectively across research and engineering teams
  • Enjoy diving deep into the weeds and hunting down the last 10-20% of performance

Qualifications / Additional Skills:
  • Experience writing highly performant GPU kernels at any level of abstraction-PTX, CUDA, HIP, Triton, or other kernel DSLs
  • Experience optimizing ML workloads for large-scale training, ideally in language model pretraining or inference environments
  • Experience with non-NVIDIA accelerator hardware, such as AMD, AWS Trainium, Google TPU, Qualcomm, ARM, Intel, and custom ASICs
  • Strong understanding of distributed training systems and parallelism schemes, including data parallelism, tensor/model parallelism, pipeline parallelism, sharding, and communication/computation overlap
  • Experience with performance engineering in other demanding parallel computing environments such as HPC, quantitative finance, scientific computing, graphics, compilers, or numerical simulation
  • Strong systems intuition around memory hierarchy, bandwidth constraints, kernel fusion, launch overhead, communication overhead, and hardware utilization
  • Experience using profiling and debugging tools to drive performance improvements
  • Familiarity with infrastructure underlying large-scale training and inference, including collective communication libraries, and runtime performance analysis
  • Background in a highly technical field such as physics, mathematics, theoretical computer science, computer science, or electrical engineering
  • Any HPC experience is a strong plus

Why Work at Zyphra:
  • Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued
  • We strongly value new and crazy ideas and are very willing to bet big on new ideas
  • We move as quickly as we can; we aim to minimize the bar to impact as low as possible
  • We all enjoy what we do and love discussing AI

Benefits and Perks:
  • Comprehensive medical, dental, vision, and FSA plans
  • Competitive compensation and 401(k) plan
  • Relocation and immigration support on a case-by-case basis
  • In-office snacks and meals provided
  • Unlimited PTO and company holidays
  • In-person team in San Francisco with a collaborative, high-energy environment

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