Senior Staff Engineer, GPU Architect, Machine Learning

Samsung•San Jose, CA

4d•Onsite

About The Position

Samsung Austin Research and Development Center (SARC) and Advanced Computing Lab (ACL) are building a center of excellence for Intellectual Property (IP) applied to high-performance computing devices. As a Senior Staff GPU Architect – Machine Learning, you will lead the design and development of innovative machine learning (ML) solutions for Samsung’s premium mobile GPUs. This role drives architectural innovation at the intersection of GPU and machine learning design to enable efficient execution of large-scale AI models. You will collaborate across hardware, software, and research teams to define new GPU features, analyze workloads, and deliver architectures that advance performance, power efficiency, and scalability for next-generation intelligent computing. You are passionate about architecting and defining ML-driven hardware features and extensions that integrate seamlessly with the graphics pipeline, advancing GPU performance, power, and functionality for AI workloads. You will analyze and model representative ML workloads to identify bottlenecks, uncover optimization opportunities, and guide architectural trade-offs that maximize PPA on applications such as LLMs, LVMs, and image classification. You will support design excellence by working on improving middleware and hardware-software integration to optimize GPU architectures for ML workloads, ensuring maximum performance and power efficiency. You will proactively seek cross-functional collaboration with GPU architects, RTL and software engineers, and research teams, to translate ML insights into production-ready GPU designs and influence long-term AI architecture roadmap. You will take initiatives on moderate-to-complex projects and help advance best practices and methodologies by staying ahead of industry trends and emerging technologies.

Requirements

11+ years of experience with a Bachelor's degree in Computer Science/Computer Engineering/relevant technical field, or 9+ years of experience with a Master's degree, or 7+ years of experience with a PhD.
Knowledge of machine learning principles and architectures
Knowledge of computer vision algorithms, AR/VR, and Ray Tracing
Knowledge of computer architectures and performance optimization
Experience with workload analysis and modeling
Strong programming skills in languages such as C++, Python, or similar
Familiarity with LLMs, LVMs, and image classification
Experience with GPU architecture and graphics pipeline
Knowledge of ML frameworks and tools (e.g., TensorFlow, PyTorch)
Ability to write clear and concise technical specifications
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

Responsibilities

Lead the design and development of innovative machine learning (ML) solutions for Samsung’s premium mobile GPUs.
Drive architectural innovation at the intersection of GPU and machine learning design to enable efficient execution of large-scale AI models.
Collaborate across hardware, software, and research teams to define new GPU features, analyze workloads, and deliver architectures that advance performance, power efficiency, and scalability for next-generation intelligent computing.
Architect and define ML-driven hardware features and extensions that integrate seamlessly with the graphics pipeline, advancing GPU performance, power, and functionality for AI workloads.
Analyze and model representative ML workloads to identify bottlenecks, uncover optimization opportunities, and guide architectural trade-offs that maximize PPA on applications such as LLMs, LVMs, and image classification.
Improve middleware and hardware-software integration to optimize GPU architectures for ML workloads, ensuring maximum performance and power efficiency.
Proactively seek cross-functional collaboration with GPU architects, RTL and software engineers, and research teams, to translate ML insights into production-ready GPU designs and influence long-term AI architecture roadmap.
Take initiatives on moderate-to-complex projects and help advance best practices and methodologies by staying ahead of industry trends and emerging technologies.