layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Sr. Field Applications Engineer, Datacenter & AI Systems Debug and Deployment Support at AMD | LAYIQ

Participate in system-level debugging of GPU, driver, networking, and AI software issues across single-node and multi-node environments, progressively taking ownership of complex debug efforts.
Support post-sales technical engagements with cloud providers, OEMs, ODMs, enterprise customers, and strategic partners.
Reproduce customer and partner issues in lab environments and analyze logs, core dumps, performance data, and system behavior to determine root causes.
Collaborate closely with engineering, product, and validation teams to drive issue resolution, validate fixes, and provide critical field feedback.
Document debug methodologies, technical findings, and solutions while contributing to internal knowledge bases and best practices.
Support large-scale cluster bring-up activities, new product introductions, and strategic customer deployments as experience grows.
Develop and deliver technical training sessions and collateral covering new products, feature enhancements, and advanced troubleshooting methodologies.
PREFERRED EXPERIENCE: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.
Knowledge of CPU and GPU architecture concepts, memory hierarchies, and system-level behavior.
Experience with Linux systems, command-line tools, system administration, and low-level debugging using tools such as kernel logs and gdb.
Relevant experience gained through internships, co-ops, academic projects, or early-career technical roles.
Working understanding of GPU architecture and GPU-accelerated workloads, including experience using AMD or NVIDIA GPU platforms.
Foundational understanding of server and accelerator architectures, including PCIe topologies, CPU-GPU interconnects, memory hierarchies, and NUMA concepts.
Exposure to AI or HPC workloads and familiarity with frameworks such as PyTorch or TensorFlow.
Proficiency in at least one programming language, such as Python, C/C++, or Java.
Experience using source control systems such as Git.
Strong analytical, problem-solving, and communication skills with the ability to work effectively alongside highly technical customer and partner teams.
WAYS TO STAND OUT: Hands-on experience with modern data center GPU platforms, including AMD Instinct™ accelerators and comparable NVIDIA solutions.
Direct experience with GPU programming frameworks such as ROCm HIP or CUDA.
Experience using performance analysis and debugging tools such as rocProf, ROCgdb, AMDuProf, or PyTorch Profiler.
Familiarity with high-performance networking technologies including InfiniBand, RoCE, and RDMA concepts.
Knowledge of distributed GPU communication frameworks such as RCCL and NCCL.
Experience with containerization and orchestration technologies such as Docker, Kubernetes, or Slurm.
Experience supporting or debugging HPC clusters, AI training environments, inference deployments, or proof-of-concept systems.
Previous experience working with OEMs, ODMs, cloud service providers, or large enterprise customers.
ACADEMIC CREDENTIALS: Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related technical discipline preferred.
LOCATION: Austin, TX
This role is not eligible for visa sponsorship.

Responsibilities

THE ROLE:

We are seeking a motivated and technically curious

Field Applications Engineer to join our Global Partner Support team. This role is ideal for an early career engineer who is motivated to grow deep expertise at the intersection of data center hardware, system software, and AI workloads.

In this role, you will work alongside experienced FAEs and engineering teams to debug, triage, and resolve complex issues involving AMD Data Center GPUs and the AI software stack. You will gradually take on increasing ownership as your technical depth and confidence grow. This is a hands-on role designed for engineers who enjoy learning through real-world problem solving in fast-paced, high-impact environments.

THE PERSON:

The ideal candidate is an intellectually curious engineer with a passion for solving challenging technical problems and building expertise across modern computing platforms. You thrive in collaborative environments, enjoy working directly with customers and partners, and are energized by opportunities to learn new technologies.

You possess strong analytical and debugging skills and are comfortable navigating issues that span hardware, software, systems, and AI workloads. You communicate effectively with both technical and non-technical audiences and approach problems with a customer-first mindset. Most importantly, you are motivated to grow into a trusted technical advisor supporting some of the industry's most advanced AI and accelerated computing deployments.

KEY RESPONSIBILITIES:

Participate in system-level debugging of GPU, driver, networking, and AI software issues across single-node and multi-node environments, progressively taking ownership of complex debug efforts.
Support post-sales technical engagements with cloud providers, OEMs, ODMs, enterprise customers, and strategic partners.
Reproduce customer and partner issues in lab environments and analyze logs, core dumps, performance data, and system behavior to determine root causes.
Collaborate closely with engineering, product, and validation teams to drive issue resolution, validate fixes, and provide critical field feedback.
Document debug methodologies, technical findings, and solutions while contributing to internal knowledge bases and best practices.
Support large-scale cluster bring-up activities, new product introductions, and strategic customer deployments as experience grows.
Develop and deliver technical training sessions and collateral covering new products, feature enhancements, and advanced troubleshooting methodologies.
PREFERRED EXPERIENCE: Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.
Knowledge of CPU and GPU architecture concepts, memory hierarchies, and system-level behavior.
Experience with Linux systems, command-line tools, system administration, and low-level debugging using tools such as kernel logs and gdb.
Relevant experience gained through internships, co-ops, academic projects, or early-career technical roles.
Working understanding of GPU architecture and GPU-accelerated workloads, including experience using AMD or NVIDIA GPU platforms.
Foundational understanding of server and accelerator architectures, including PCIe topologies, CPU-GPU interconnects, memory hierarchies, and NUMA concepts.
Exposure to AI or HPC workloads and familiarity with frameworks such as PyTorch or TensorFlow.
Proficiency in at least one programming language, such as Python, C/C++, or Java.
Experience using source control systems such as Git.
Strong analytical, problem-solving, and communication skills with the ability to work effectively alongside highly technical customer and partner teams.
WAYS TO STAND OUT: Hands-on experience with modern data center GPU platforms, including AMD Instinct™ accelerators and comparable NVIDIA solutions.
Direct experience with GPU programming frameworks such as ROCm HIP or CUDA.
Experience using performance analysis and debugging tools such as rocProf, ROCgdb, AMDuProf, or PyTorch Profiler.
Familiarity with high-performance networking technologies including InfiniBand, RoCE, and RDMA concepts.
Knowledge of distributed GPU communication frameworks such as RCCL and NCCL.
Experience with containerization and orchestration technologies such as Docker, Kubernetes, or Slurm.
Experience supporting or debugging HPC clusters, AI training environments, inference deployments, or proof-of-concept systems.
Previous experience working with OEMs, ODMs, cloud service providers, or large enterprise customers.
ACADEMIC CREDENTIALS: Bachelor's or Master's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related technical discipline preferred.
LOCATION: Austin, TX
This role is not eligible for visa sponsorship.

Sr. Field Applications Engineer, Datacenter & AI Systems Debug and Deployment Support

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