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Cuda Kernel Engineer Jobs in Philadelphia, PA (NOW HIRING)

YO AI Labs

CUDA Developer - Remote

Philadelphia, PA · Remote

$60 - $100/hr

Strong expertise in CUDA programming and GPU kernel optimization . * Advanced C++ development skills. * Hands-on experience with GLSL and WebGPU . * Experience using GPU profiling tools such as ...

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Cuda Kernel Engineer information

What is a CUDA Kernel Engineer?

Cuda Kernel Engineers are specialized software developers who design, implement, and optimize parallel computing algorithms using NVIDIA's CUDA platform. They write 'kernels,' which are functions that run on Graphics Processing Units (GPUs) to accelerate computational tasks in areas such as machine learning, scientific simulations, and graphics rendering. These engineers need strong skills in C/C++ programming, GPU architecture, and performance optimization techniques. Their work is crucial for applications that require high-speed data processing and efficient resource utilization.

What skills and qualifications are needed to be a CUDA Kernel Engineer?

To thrive as a CUDA Kernel Engineer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid foundation in GPU architectures, typically supported by a degree in computer science or a related field. Expertise in NVIDIA CUDA toolkits, GPU profiling tools like Nsight, and familiarity with version control systems are essential. Analytical thinking, problem-solving abilities, and effective collaboration skills help engineers optimize code and work well within development teams. These skills and qualities are crucial for delivering high-performance, scalable GPU solutions in computationally intensive applications.

What are common challenges faced by CUDA Kernel Engineers when optimizing GPU code for performance?

Cuda Kernel Engineers often encounter challenges such as managing memory hierarchy efficiently, minimizing data transfer between host and device, and avoiding thread divergence. Ensuring optimal occupancy and maximizing parallelism while preventing bottlenecks like bank conflicts or uncoalesced memory access are also key concerns. Collaborating closely with software architects and data scientists is common, as solutions frequently require balancing algorithmic accuracy with hardware limitations. Addressing these challenges requires continuous profiling, testing, and iterative optimization.

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Cuda Kernel Engineer jobs near you

CUDA Software Engineer - Remote

YO AI Labs

Philadelphia, PA • Remote

$60 - $100/hr

Full-time

Posted 5 days ago

Job description

CUDA Engineering Expert

Job Type: Contractor
Location: Remote

Job Overview

We are seeking experienced CUDA Engineering Experts to support a cutting-edge GPU optimization project. 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, profile, and optimize GPU kernels using CUDA and profiling tools.

  • Identify performance bottlenecks and develop targeted optimization strategies.

  • Refactor C++ and CUDA code for efficiency and maintainability.

  • Develop shader logic using GLSL and WebGPU.

  • Document optimization processes, findings, and performance improvements.

  • Contribute to GPU architecture and performance discussions.

  • Collaborate with remote, cross-functional teams.

Required Qualifications

  • Strong expertise in CUDA programming and GPU kernel optimization.

  • Advanced C++ development skills.

  • Hands-on experience with GLSL and WebGPU.

  • Experience using GPU profiling tools such as NVIDIA Nsight or similar.

  • Strong understanding of GPU performance and architecture.

  • Excellent analytical, problem-solving, and technical communication skills.

  • Ability to work effectively in a remote environment.

Preferred Qualifications

  • Experience with high-performance computing or GPU-accelerated applications.

  • Experience optimizing workloads across different GPU architectures.

  • Background in graphics, compute shaders, or AI/ML acceleration.