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Gpu Performance Engineer Jobs in Philadelphia, PA

YO AI Labs

CUDA Developer - Remote

Philadelphia, PA · Remote

$60 - $100/hr

You will apply your expertise in CUDA, C++, and GPU programming to analyze, optimize, and improve high-performance GPU kernels. No prior AI experience is required. Key Responsibilities * Analyze ...

New

YO AI Labs

GPU Programmer - Remote

Philadelphia, PA · Remote

$60 - $85/hr

You will apply your expertise in GPU programming, performance optimization, and C++ development to build high-performance solutions. Key Responsibilities * Design, implement, and optimize GPU ...

ECS

Software Engineer III

Moorestown, NJ · On-site

$56.75 - $76.25/hr

The engineer will work closely with radar systems engineers, integration teams, and test personnel to implement high-performance radar software, including GPU-accelerated processing where applicable.

ECS

Software Engineer III

Moorestown, NJ · On-site

$56.75 - $76.25/hr

They are seeking a Software Engineer III to design, develop, optimize, test, and document real-time ... high-performance radar software, including GPU-accelerated processing where applicable ...

Skylimit Systems

Radar Software Engineer

Moorestown, NJ · On-site

$110K - $200K/yr

Working as part of an Agile engineering team, this role will contribute across the full software ... and performance optimization. * Familiarity with GPU development and parallel computing (CUDA ...

ECS

Software Engineer

Moorestown, NJ · On-site

$98K - $147K/yr

The engineer will work closely with radar systems engineers, integration teams, and test personnel to implement high-performance radar software, including GPU-accelerated processing where applicable.

ECS

Software Engineer V

Moorestown, NJ · On-site

The engineer will work closely with radar systems engineers, integration teams, and test personnel to implement high-performance radar software, including GPU-accelerated processing where applicable.

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Gpu Performance Engineer information

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How much do gpu performance engineer jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for gpu performance 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 is a GPU performance engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

What are some common challenges faced by a GPU performance engineer when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What are the key skills and qualifications needed to thrive as a GPU performance engineer, and why are they important?

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

What are popular job titles related to Gpu Performance Engineer jobs in Philadelphia, PA?

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What cities near Philadelphia, PA are hiring for Gpu Performance Engineer jobs?

Cities near Philadelphia, PA with the most Gpu Performance Engineer job openings:

Gpu Performance Engineer jobs near you

GPU Performance Engineer | Experienced Hire

Bala Cynwyd, PA • On-site

SIG Susquehanna
See more jobs at SIG Susquehannaexternal

$120 - $160/hr

Other

Posted 19 days ago

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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