Samsung Electronics

Senior Engineer, Machine Learning Application Developer

Samsung Electronics
US San Jose, CA, US
Onsite $124k–$208k · announced 2026-06-18
What this role pays in San Jose
$78K - $148K
Low
$78K
Median
$111K
High
$148K
Official salary benchmark · BLS OEWS 2025

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Job description

**Position Summary** 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!**Role and Responsibilities** 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 efficie

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