Research Intern (Hardware / Software / System Design)

Key Skills

Aartificial intelligence, C Programming, C++, Design, FPGAs, GPUs, Machine learning techniques, Python Programming, RTL design

Job Description

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. 

AMD together we advance_

THE TEAM

We are part of the CTO organization in AMD, and focus on innovation, differentiation, and the de-risking of technology in future systems and products. Our technical focus is to research and evaluate next-generation networking solutions for AI and machine learning, data center networking and heterogenous computing, among others. Our work spans research and development in both hardware and software aspects of future systems.

THE ROLE

We are looking for top-tier interns who can participate in various aspects of a system design including but not limited to architecture specification, RTL design, verification, simulators, hands-on FPGA deployment, design space exploration, and software stacks. We expect to have a practical focus for the internship involving systems comprised of CPUs, GPUs, network-attached and/or PCIe-attached FPGAs and SmartNICs. The intern may also be expected to leverage Heterogenous Adaptive Compute Cluster (HACC) at NUS Singapore, which is equipped with the latest hardware including high-end servers with AMD CPUs and GPUs, Alveo accelerator cards and high-speed networking.

PREFFERED EXPERIENCE

• Candidates with strong experience in FPGA or ASIC-based RTL design and verification, experience with FPGA deployment and programming, FPGA based acceleration of cloud applications, programmable data plane, machine learning training on GPUs and awareness of hardware/software co-design should apply.
• Strong hands-on coding skills, e.g., C/C++, Verilog HDL, Python, P4, ML frameworks, etc.
• Knowledge of distributed systems, data center networking, high performance computing, and distributed machine learning training is valuable.
• Research experience and publications in related areas (e.g., computing systems, networking, hardware/software co-design, machine learning) are welcome.

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