Senior Engineer, Machine Learning Application Developer

Samsung Electronics•San Diego, CA

2d•$124,000 - $208,400•Onsite

About The Position

Samsung, a world leader in advanced semiconductor technology, is founded on a simple philosophy – the endless pursuit of excellence will create a better world for all. At Samsung Austin Research and Development Center (SARC) and Advanced Computing Lab (ACL), we are building a center of excellence for Intellectual Property (IP) that is applied to high-performance computing devices (mobile, automotive, and other custom market segments) consumed by millions of people around the world. Come build with us! As a Machine Learning Application Developer, you will develop neural rendering applications and machine learning (ML) software that enable efficient execution of AI workloads on Samsung’s premium mobile GPUs. In this individual contributor role, you will contribute to the development of software solutions that bridge machine learning workloads and GPU hardware capabilities. Working closely with hardware, software, and architecture teams, you will help optimize performance, efficiency, and resource utilization to support next-generation intelligent computing experiences. You help developing and optimizing neural rendering applications, API-level software, and ML operator implementations, including GEMM, convolution, activations, and related workloads, using Vulkan, OpenGL, and OpenCL to enable efficient execution of ML and graphics workloads on Samsung GPU platforms. You analyze software performance and hardware resource utilization to identify bottlenecks and optimize application performance, efficiency, and scalability across a variety of ML workloads. You proactively seek collaborations with GPU architects, software engineers, and hardware teams to understand underlying hardware constraints and translate performance insights into optimized software solutions. You leverage low-level performance analysis techniques, including assembly-level investigation when needed, to help improve execution efficiency and maximize GPU utilization. You take initiatives on moderate-to-complex projects and help advance best practices and methodologies by staying current with the latest advancements in machine learning, neural rendering, and GPU technologies.

Requirements

3+ years of experience with a Bachelor's Degree in Computer Science, Computer Engineering, or comparable field, or 2+ years of experience with a Master’s Degree, or Ph.D.
Strong programming skills in C, C++, and Python.
Proficiency with API-level programming using in Vulkan, OpenGL, OpenCL, and machine learning frameworks such as PyTorch and TensorFlow.
Understanding of GPU hardware architecture and experience with low-level performance profiling, analysis, and optimization.
Hands-on experience developing neural rendering applications at the API level.
Working knowledge of machine learning operators and workloads, including GEMM, convolution, activations, and related computational kernels.
Ability to analyze hardware resource constraints and bottlenecks and develop software optimizations that improve performance and efficiency.
Strong analytical and problem-solving skills, with the ability to identify bottlenecks and propose data-driven solutions.
Excellent communication and collaboration skills, with the ability to navigate ambiguity in a fast-paced, global team environment.

Nice To Haves

Working knowledge of assembly-level analysis, debugging, or optimization is preferred.

Responsibilities

Develop neural rendering applications and machine learning (ML) software that enable efficient execution of AI workloads on Samsung’s premium mobile GPUs.
Contribute to the development of software solutions that bridge machine learning workloads and GPU hardware capabilities.
Optimize performance, efficiency, and resource utilization to support next-generation intelligent computing experiences.
Develop and optimize neural rendering applications, API-level software, and ML operator implementations, including GEMM, convolution, activations, and related workloads, using Vulkan, OpenGL, and OpenCL to enable efficient execution of ML and graphics workloads on Samsung GPU platforms.
Analyze software performance and hardware resource utilization to identify bottlenecks and optimize application performance, efficiency, and scalability across a variety of ML workloads.
Collaborate with GPU architects, software engineers, and hardware teams to understand underlying hardware constraints and translate performance insights into optimized software solutions.
Leverage low-level performance analysis techniques, including assembly-level investigation when needed, to improve execution efficiency and maximize GPU utilization.
Take initiatives on moderate-to-complex projects and advance best practices and methodologies by staying current with the latest advancements in machine learning, neural rendering, and GPU technologies.