PMTS Software Development Engineer

Advanced Micro Devices, Inc•Bellevue, WA

1d•Onsite

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them. AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward.

Requirements

Master’s degree or foreign equivalent in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or related field and five (5) years of experience in the job offered or closely related engineering role.
OR Bachelor’s degree or foreign equivalent in Computer Science, Computer Engineering, Electrical Engineering, Software Engineering, or related field and seven (7) years of progressive post-baccalaureate experience in the job offered or closely related engineering role.
Two (2) years of experience in designing, implementing, and testing computer hardware.
Two (2) years of experience working with architecture and software teams to write specifications for the microarchitecture design.
Two (2) years of experience with power design concepts like multiple power domain, power gating, and data retention.
Two (2) years of experience with Verilog RTL coding.
Two (2) years of experience with UVM testbench.
Two (2) years of experience with VLSI Design.
Two (2) years of experience with scripting languages, including Perl, Python, and TCL/TK.
Two (2) years of experience with FPGA.

Responsibilities

Develop and optimize low-level GPU kernels to accelerate inference and training of large machine learning models.
Maximize the computational efficiency and reduce execution time while ensuring model accuracy.
Design and implement strategies for distributed model training and inference across multiple GPUs and nodes.
Address data parallelism and model parallelism challenges to fully utilize available resources.
Profile and analyze system and application performance to identify bottlenecks and areas for improvement.
Use profiling tools to understand and optimize hardware resource utilization.
Leverage parallel computing techniques to improve the scalability and performance of machine learning workloads.
Implement multi-threading and GPU synchronization techniques.
Explore and apply model quantization techniques to reduce memory and computation overhead, especially for edge and cloud deployment.
Develop benchmarks and testing procedures to assess the performance and stability of optimized models and frameworks.
Ensure that the solutions meet or exceed the defined performance criteria.
Collaborate closely with machine learning researchers, software engineers, and infrastructure teams to integrate optimized kernels and solutions into production systems.
Create detailed documentation of optimizations, best practices, and implementation guidelines to facilitate knowledge sharing and maintainable code.
Utilize knowledge of computers and electronics, including circuit boards, processors, chips, and electronic equipment, as well as knowledge of design techniques, tools, and principles.
Apply knowledge of engineering principles, best practices, and technologies to the design, development, and testing of various company proprietary products.