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Freelance High Performance Computing Engineer Jobs in Philadelphia, PA

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 ...

Cornelis Networks

Thermal Design Engineer

Wayne, PA · Hybrid

Cornelis Networks is hiring a talented Entry-Level Thermal Engineer with a foundational ... Exposure to high-performance computing (HPC) or data center environments. Location This position is ...

New

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

Freelance High Performance Computing Engineer information

See Philadelphia, PA salary details

$54K

$132.5K

$195.3K

How much do freelance high performance computing engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for freelance high performance computing engineer in Philadelphia, PA is $132,542.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,500.00 and $148,800.00 per year, depending on experience, location, and employer.

How do freelance high performance computing engineers typically collaborate with client teams during projects?

Freelance HPC Engineers often work closely with client engineering, research, or IT teams to design, implement, and optimize computational solutions. Collaboration usually occurs through regular virtual meetings, code reviews, and progress updates to ensure alignment with project goals and technical requirements. Clear communication and documentation are essential, as freelancers may need to integrate their work into larger systems or hand off projects to in-house teams. Building strong relationships and understanding the client's workflow help ensure successful project delivery and can lead to ongoing opportunities.

What is a freelance high performance computing engineer?

A Freelance High Performance Computing (HPC) Engineer is a professional who specializes in designing, implementing, and optimizing computing systems that handle complex, large-scale computations. They work independently or on a contract basis for different organizations, helping to develop and maintain supercomputers, clusters, and parallel processing applications. Their expertise is often sought in fields like scientific research, finance, artificial intelligence, and engineering where processing large datasets quickly is essential. Freelancers in this field typically possess strong programming skills, knowledge of HPC architectures, and experience with performance tuning and troubleshooting.

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

To thrive as a Freelance High Performance Computing Engineer, you need expertise in parallel programming, cluster management, and a strong background in computer science or engineering. Familiarity with tools such as MPI, OpenMP, Linux environments, and cloud-based HPC platforms, along with certifications in cloud services or HPC technologies, is highly beneficial. Excellent problem-solving, project management, and communication skills set top freelancers apart when working with diverse clients. These competencies ensure the delivery of optimized, scalable solutions and effective collaboration in complex technical projects.

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

AspectFreelance High Performance Computing EngineerFreelance Data Scientist
CredentialsAdvanced degrees in computer science, engineering, or related fields; knowledge of HPC systemsDegree in data science, statistics, or related fields; proficiency in programming and analytics
Work EnvironmentSpecialized computing clusters, research labs, or cloud HPC platformsData analysis environments, cloud platforms, and business analytics tools
Industry UsageResearch institutions, scientific computing, engineering simulations
Search & Comparison IntentFocus on high-performance computing tasks, technical skills

While both roles involve advanced technical skills, Freelance High Performance Computing Engineers specialize in optimizing and managing large-scale computing resources for scientific and engineering applications. Freelance Data Scientists focus on analyzing data to extract insights for business or research purposes. The key difference lies in their core focus: HPC engineers work with hardware and system performance, whereas data scientists work with data analysis and modeling.

What are the most commonly searched types of High Performance Computing Engineer jobs in Philadelphia, PA? The most popular types of High Performance Computing Engineer jobs in Philadelphia, PA are:
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What job categories do people searching Freelance High Performance Computing Engineer jobs in Philadelphia, PA look for? The top searched job categories for Freelance High Performance Computing Engineer jobs in Philadelphia, PA are:
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Freelance 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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