AI Cluster Validation Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING 

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.  Together, we advance your career.  

THE ROLE:  

We are looking for a Senior Engineer to drive validation of next-generation AI cluster solutions. In this role, you will be at the forefront of optimizing GPU cluster, working across the full system stack to ensure our solutions meet the demanding requirements of large-scale AI workloads. The primary focus of this role is on the RDMA fabric at the heart of these systems. Understanding data flows between GPUs, NICs, and the cluster network to optimize performance at scale.

 

The ideal candidate brings strong expertise in GPU architectures, parallel computing, and hands-on experience validating complex, high-performance systems.

 

This is a high-impact engineering role for someone who thrives at the intersection of hardware, networking, and systems software and who wants to shape the performance and reliability of AI infrastructure at scale.

 

THE PERSON:  

You are a seasoned engineer who thrives on hands-on problem-solving. Equally comfortable shaping long-term strategy and diving deep into complex hardware, firmware, and driver issues. You communicate clearly across teams, take full ownership of your work, and bring the drive and work ethic to see tough problems through to resolution.

Our team is built on a culture of technical innovation and continuous career development, where your impact will be felt across performance, automation, and development.

KEY RESPONSIBILITIES:  

• Scalability Testing: Evaluate GPU cluster scalability through rigorous testing across diverse workloads, cluster sizes, configurations, and networking technologies including RoCE.
• Benchmarking & Performance Profiling: Develop and execute comprehensive benchmarking strategies to establish performance baselines, identify bottlenecks, and generate actionable insights for improvement.
• Performance Tuning: Implement optimization strategies across protocol enhancements, load balancing, and parallel processing to drive measurable gains in RDMA throughput, latency, and collective communications.
• NIC & Cluster Optimization: Collaborate with hardware and software teams to enhance GPU cluster performance end-to-end, with a focus on the NIC-to-network data path.
• Cross-functional Collaboration: Partner closely with hardware engineers, software developers, and system architects to integrate performance improvements into cluster architecture.
• Documentation: Produce clear, detailed documentation of performance analysis, tuning methodologies, and outcomes for both internal teams and senior stakeholders.
• Continuous Learning: Stay current with advances in GPU architectures, parallel processing, and emerging networking technologies to inform ongoing improvement efforts.

PREFERRED EXPERIENCE:  

• Proven experience in optimizing the performance of GPU clusters.
• RDMA network configuration, troubleshooting and performance tuning. 
• Strong understanding of GPU architectures, parallel computing concepts, and network protocols.
• Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis.
• Experience with system level performance analysis tools and methodologies for GPU clusters.
• Analytical mindset with excellent problem-solving and debug skills.
• Excellent communication and collaboration skills for effective teamwork.

ACADEMIC CREDENTIALS:  

• Bachelors or Masters degree in electrical or computer engineering preferred

 

LOCATION: Austin, TX, Santa Clara CA, Seattle WA, Secaucus NJ

 

This role is not eligible for visa sponsorship.

 

#LI-SC3

Benefits offered are described:  AMD benefits at a glance.

 

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

 

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available here.

 

This posting is for an existing vacancy.

THE ROLE:  

We are looking for a Senior Engineer to drive validation of next-generation AI cluster solutions. In this role, you will be at the forefront of optimizing GPU cluster, working across the full system stack to ensure our solutions meet the demanding requirements of large-scale AI workloads. The primary focus of this role is on the RDMA fabric at the heart of these systems. Understanding data flows between GPUs, NICs, and the cluster network to optimize performance at scale.

 

The ideal candidate brings strong expertise in GPU architectures, parallel computing, and hands-on experience validating complex, high-performance systems.

 

This is a high-impact engineering role for someone who thrives at the intersection of hardware, networking, and systems software and who wants to shape the performance and reliability of AI infrastructure at scale.

 

THE PERSON:  

You are a seasoned engineer who thrives on hands-on problem-solving. Equally comfortable shaping long-term strategy and diving deep into complex hardware, firmware, and driver issues. You communicate clearly across teams, take full ownership of your work, and bring the drive and work ethic to see tough problems through to resolution.

Our team is built on a culture of technical innovation and continuous career development, where your impact will be felt across performance, automation, and development.

KEY RESPONSIBILITIES:  

• Scalability Testing: Evaluate GPU cluster scalability through rigorous testing across diverse workloads, cluster sizes, configurations, and networking technologies including RoCE.
• Benchmarking & Performance Profiling: Develop and execute comprehensive benchmarking strategies to establish performance baselines, identify bottlenecks, and generate actionable insights for improvement.
• Performance Tuning: Implement optimization strategies across protocol enhancements, load balancing, and parallel processing to drive measurable gains in RDMA throughput, latency, and collective communications.
• NIC & Cluster Optimization: Collaborate with hardware and software teams to enhance GPU cluster performance end-to-end, with a focus on the NIC-to-network data path.
• Cross-functional Collaboration: Partner closely with hardware engineers, software developers, and system architects to integrate performance improvements into cluster architecture.
• Documentation: Produce clear, detailed documentation of performance analysis, tuning methodologies, and outcomes for both internal teams and senior stakeholders.
• Continuous Learning: Stay current with advances in GPU architectures, parallel processing, and emerging networking technologies to inform ongoing improvement efforts.

PREFERRED EXPERIENCE:  

• Proven experience in optimizing the performance of GPU clusters.
• RDMA network configuration, troubleshooting and performance tuning. 
• Strong understanding of GPU architectures, parallel computing concepts, and network protocols.
• Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis.
• Experience with system level performance analysis tools and methodologies for GPU clusters.
• Analytical mindset with excellent problem-solving and debug skills.
• Excellent communication and collaboration skills for effective teamwork.

ACADEMIC CREDENTIALS:  

• Bachelors or Masters degree in electrical or computer engineering preferred

 

LOCATION: Austin, TX, Santa Clara CA, Seattle WA, Secaucus NJ

 

This role is not eligible for visa sponsorship.

 

#LI-SC3

Benefits offered are described:  AMD benefits at a glance.

 

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

 

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available here.

 

This posting is for an existing vacancy.