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Internship High Performance Computing Engineer Jobs in Philadelphia, PA

Cornelis Networks

Thermal Design Engineer

Wayne, PA · Hybrid

... internship, co-op, or academic lab experience qualifies). * Hands-on experience in a lab ... Exposure to high-performance computing (HPC) or data center environments. Location This position is ...

New

Cornelis Networks

Mechanical Engineer

Wayne, PA · Hybrid

The mechanical engineer will be involved in all phases of mechanical architecture and hardware ... Experience with high-performance computing (HPC) or data center hardware environments.

New

Holtec International

Senior Systems Engineer

Camden, NJ · On-site

$135K - $155K/yr

A core function of this role includes HPC Cluster Administration supporting a high-performance computing environment used for scientific, engineering, and analytics workloads. In this role, you will ...

Introl

Data Center Technician

Philadelphia, PA · On-site

$30 - $36/hr

... high-performance computing deployments, supporting the advancement of AI computing with precision, speed, and minimal downtime. Our network of 1,000+ field engineers operates globally, tackling the ...

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All JobsHigh Performance Computing Engineer JobsInternship High Performance Computing Engineer JobsInternship High Performance Computing Engineer Jobs in Philadelphia, PA

Internship High Performance Computing Engineer information

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How much do internship high performance computing engineer jobs pay per hour?

As of Aug 8, 2026, the average hourly pay for internship high performance computing engineer in Philadelphia, PA is $60.65, according to ZipRecruiter salary data. Most workers in this role earn between $49.71 and $68.65 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an internship high performance computing engineer?

To thrive as an Internship High Performance Computing Engineer, you need a solid background in computer science fundamentals, programming (especially in C/C++ or Python), and a familiarity with parallel computing concepts, often supported by coursework or relevant project experience. Experience with Linux environments, HPC clusters, and distributed computing frameworks, as well as tools like MPI, OpenMP, or Slurm, is commonly required. Strong problem-solving skills, attention to detail, and the ability to collaborate effectively within technical teams help interns stand out. These skills ensure you can efficiently support computational research, resolve technical challenges, and contribute meaningfully to HPC projects.

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

AspectInternship High Performance Computing EngineerInternship Data Scientist
Required SkillsProgramming (C++, Python), parallel computing, HPC systemsStatistics, machine learning, data analysis, Python/R
Work EnvironmentResearch labs, tech companies, academia with focus on HPC systemsTech firms, finance, healthcare, research institutions
Industry UsageHigh-performance computing projects, scientific simulationsData analysis, predictive modeling, business insights

Internship High Performance Computing Engineers focus on developing and optimizing computational systems for large-scale scientific and engineering problems, requiring skills in parallel programming and HPC environments. In contrast, Internship Data Scientists analyze data to extract insights, using statistical and machine learning techniques. Both roles are valuable in tech and research sectors but differ in technical focus and daily tasks.

What is an internship high performance computing engineer?

An Internship High Performance Computing (HPC) Engineer is a student or early-career professional who works with advanced computing systems designed for processing large data sets and complex calculations at high speeds. During the internship, they assist in developing, optimizing, and maintaining HPC infrastructure, software, or applications used in scientific research, engineering, or data analysis. The role often involves learning about parallel computing, cluster management, and performance tuning, while gaining hands-on experience with cutting-edge technologies. Interns work under the supervision of experienced HPC engineers, contributing to projects that advance computational capabilities in various fields.

What types of projects can I expect to work on as an internship high performance computing engineer?

As an Internship High Performance Computing (HPC) Engineer, you will typically contribute to projects involving optimization of scientific applications, performance analysis, and cluster management. Interns often assist with benchmarking software, troubleshooting issues in parallel computing environments, and supporting researchers with technical solutions. You'll likely collaborate closely with senior HPC engineers, system administrators, and academic researchers to ensure efficient use of computing resources. This hands-on experience provides valuable insight into real-world challenges faced in HPC environments and helps build a strong foundation for future roles in the field.
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Internship High Performance Computing Engineer jobs near you

GPU Performance Engineer | Experienced Hire

SIG Susquehanna

Bala Cynwyd, PA • On-site

$120 - $160/hr

Other

Posted 3 days ago

New

Job description

Overview

We are looking for a GPU Performance Engineer to build highly optimized CUDA kernels for low-latency inference. This role focuses on workloads where off-the-shelf runtimes and vendor libraries do not fully exploit the structure of the model, and where custom kernels, memory layouts, and execution strategies can deliver meaningful gains.

You will work closely with quantitative researchers and engineers to understand model structure, identify computational bottlenecks, and convert mathematical ideas into production-grade GPU implementations. Using your understanding of GPU hardware, you will help shape models that are both mathematically effective and efficient to run. The problems span compact neural networks, tree-based models, and other structured inference workloads where latency, throughput, and efficiency all matter.

This role is a strong fit for someone who enjoys low-level optimization, performance analysis, and translating abstract models into hardware-efficient code.

What you’ll do
  • Design, implement, and optimize custom CUDA kernels for latency-critical inference workloads
  • Develop fine-grained GPU implementations tailored to specific model structures
  • Analyze quantitative research models and computational bottlenecks to identify opportunities for parallelization and hardware-efficient execution
  • Collaborate directly with quantitative researchers to translate mathematical models into high-performance computing pipelines
  • Optimize end-to-end inference performance through kernel tuning, memory‑layout design, execution strategy, I/O optimization, and precision tradeoffs
  • Profile and benchmark GPU performance
  • Improve latency and throughput in production inference systems
  • Contribute to GPU architecture decisions and performance best practices
What we’re looking for
  • Strong proficiency in writing and optimizing CUDA kernels
  • Solid programming experience in C/C++ (preferred)
  • Deep understanding of GPU architecture, including memory hierarchy, SIMT execution, occupancy, and latency/throughput tradeoffs
  • Ability to reason about numerical stability, precision, performance tradeoffs, and how model design choices affect hardware efficiency
  • Strong problem‑solving skills and comfort working with low-level systems
Preferred qualifications
  • PhD in mathematics, physics, computer science, engineering, or a related quantitative field
  • Strong background in linear algebra, probability, numerical methods, or scientific computing
  • Experience working with quantitative research teams or financial models
  • Demonstrated ability to improve real-world inference performance beyond baseline framework or library implementations
  • Familiarity with PTX-level behavior, tensor‑core utilization, or architecture-specific tuning
  • Exposure to ONNX Runtime, TensorRT, Triton, TVM, or similar systems
  • Exposure to neural networks, tree-based models (e.g., LightGBM), state‑space models (e.g., Mamba architectures), and experience with kernel fusion, custom operators, model compilation, or graph-level optimization
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