Beyond these core responsibilities, you will have opportunities to explore and develop new agentic technologies and apply them to a broad range of emerging AI and high-performance computing use cases.
Key Responsibilities
Design and develop agentic AI frameworks supporting planning, tool use, memory, reasoning, and long-running workflows.
Build automation for environment setup, containerization, dependency management, and reproducible workload deployment.
Compose and launch scheduler-based workloads using resource-aware configurations and appropriate operational policies.
Investigate failures using logs, telemetry, and system information, and develop safe and reliable automated remediation approaches.
Profile applications and workloads to identify performance bottlenecks and opportunities for optimization.
Benchmark and validate performance improvements using robust and reproducible methodologies.
Contribute to optimization workflows spanning applications, libraries, runtimes, compilers, and GPU kernels.
Build appropriate testing, observability, validation, rollback, and performance regression mechanisms.
Explore new approaches to agentic AI and help apply emerging technologies to real-world engineering challenges.
What Success Could Look Like in Your First 6 Months
Working collaboratively with the wider engineering team, you could
Deliver an initial onboarding solution covering environment setup, containers, workload composition and launch, failure triage, and reproducible execution.
Deploy the solution against a production workload and help reduce time to first successful execution while identifying measurable performance improvements.
Develop an optimization workflow for a performance-critical kernel or workload, incorporating correctness testing, safe rollback, and performance regression checks.
Build strong working relationships across platform, performance, compiler, runtime, and AI engineering teams.
Identify opportunities where agentic AI can simplify or accelerate additional engineering workflows.
Collaboration
You will collaborate with multidisciplinary engineering teams across the organization.
This may include working with
Platform engineering teams to enable reliable deployment across shared compute and GPU infrastructure.
Performance engineering teams to establish meaningful benchmarks, measurements, and performance validation.
Compiler engineering teams to identify and implement code-generation or compiler-level improvements.
Runtime engineering teams to improve tuning, debugging, profiling, and observability capabilities.
AI and application teams to identify new opportunities for agentic systems and translate research or emerging technologies into practical solutions.
Required Qualifications
We value skills developed through a variety of professional, academic, and practical experiences. You may be a strong fit if you have experience in several of the following areas:
A Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, a related technical discipline, or equivalent practical experience.
Experience developing and deploying agentic AI or autonomous software systems involving areas such as orchestration, planning, tool use, memory, or long-running workflows.
Experience building or integrating LLM inference and serving solutions, either on-premises or through cloud-based services.
Strong programming experience in Python, combined with experience in at least one systems programming language such as C, C++, Rust, or Go.
Experience building reliable production software using practices such as evaluation frameworks, regression testing, CI/CD, observability, and structured debugging.
Experience connecting automation or AI systems with real-world engineering tools, such as build and test systems, code execution environments, schedulers, APIs, or telemetry platforms.
A collaborative approach to solving complex technical problems across different engineering disciplines.
Preferred Experience
Experience in one or more of the following areas would be beneficial, but you do not need to have experience in every area to be considered:
HPC environments and workflows, including technologies such as Slurm, Kubernetes, multi-node GPU workloads, or container platforms.
GPU profiling and performance analysis using ROCm, CUDA, or similar performance and tracing tools.
LLM training, fine-tuning, or evaluation, including approaches such as supervised fine-tuning or reinforcement learning.
GPU kernel development, tuning, or code generation using technologies such as Triton, CUDA, or HIP.
Performance optimization techniques including GEMM/MatMul optimization, operator fusion, mixed precision, and numerical validation.
Compiler and intermediate-representation technologies such as MLIR, LLVM, XLA, or related compiler frameworks.
Performance portability and optimization across different hardware architectures.
Developing or evaluating emerging agentic AI techniques and applying them to complex engineering workflows.
We recognize that strong candidates may bring different combinations of experience and expertise. If you are excited about the opportunity but do not meet every preferred qualification, we encourage you to apply. We value diverse perspectives, experiences, and approaches to problem-solving and are committed to creating an inclusive environment where people can contribute, develop, and succeed.
#LI-MH3
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.
Senior Agentic Systems & Applications Engineer
Role Description
We are looking for a Senior Agentic Systems & Applications Engineer to help build advanced agentic AI systems that make HPC and GPU workloads easier to deploy, manage, optimize, and scale.
In this role, you will design, develop, and deploy intelligent systems capable of setting up environments, composing and launching workloads, diagnosing failures, profiling performance, and identifying and implementing optimizations across applications, libraries, and CI/performance environments.
You will work across areas including agentic AI architecture, planning and tool use, LLM inference and serving, HPC infrastructure, containers, schedulers, performance analysis, runtimes, and compilers. Depending on the workload, you may also contribute to kernel-level optimization, tuning, or code generation while ensuring solutions are reliable, reproducible, observable, and technically robust.
Beyond these core responsibilities, you will have opportunities to explore and develop new agentic technologies and apply them to a broad range of emerging AI and high-performance computing use cases.
Key Responsibilities
Design and develop agentic AI frameworks supporting planning, tool use, memory, reasoning, and long-running workflows.
Build automation for environment setup, containerization, dependency management, and reproducible workload deployment.
Compose and launch scheduler-based workloads using resource-aware configurations and appropriate operational policies.
Investigate failures using logs, telemetry, and system information, and develop safe and reliable automated remediation approaches.
Profile applications and workloads to identify performance bottlenecks and opportunities for optimization.
Benchmark and validate performance improvements using robust and reproducible methodologies.
Contribute to optimization workflows spanning applications, libraries, runtimes, compilers, and GPU kernels.
Build appropriate testing, observability, validation, rollback, and performance regression mechanisms.
Explore new approaches to agentic AI and help apply emerging technologies to real-world engineering challenges.
What Success Could Look Like in Your First 6 Months
Working collaboratively with the wider engineering team, you could
Deliver an initial onboarding solution covering environment setup, containers, workload composition and launch, failure triage, and reproducible execution.
Deploy the solution against a production workload and help reduce time to first successful execution while identifying measurable performance improvements.
Develop an optimization workflow for a performance-critical kernel or workload, incorporating correctness testing, safe rollback, and performance regression checks.
Build strong working relationships across platform, performance, compiler, runtime, and AI engineering teams.
Identify opportunities where agentic AI can simplify or accelerate additional engineering workflows.
Collaboration
You will collaborate with multidisciplinary engineering teams across the organization.
This may include working with
Platform engineering teams to enable reliable deployment across shared compute and GPU infrastructure.
Performance engineering teams to establish meaningful benchmarks, measurements, and performance validation.
Compiler engineering teams to identify and implement code-generation or compiler-level improvements.
Runtime engineering teams to improve tuning, debugging, profiling, and observability capabilities.
AI and application teams to identify new opportunities for agentic systems and translate research or emerging technologies into practical solutions.
Required Qualifications
We value skills developed through a variety of professional, academic, and practical experiences. You may be a strong fit if you have experience in several of the following areas:
A Master’s or PhD in Computer Science, Computer Engineering, Electrical Engineering, a related technical discipline, or equivalent practical experience.
Experience developing and deploying agentic AI or autonomous software systems involving areas such as orchestration, planning, tool use, memory, or long-running workflows.
Experience building or integrating LLM inference and serving solutions, either on-premises or through cloud-based services.
Strong programming experience in Python, combined with experience in at least one systems programming language such as C, C++, Rust, or Go.
Experience building reliable production software using practices such as evaluation frameworks, regression testing, CI/CD, observability, and structured debugging.
Experience connecting automation or AI systems with real-world engineering tools, such as build and test systems, code execution environments, schedulers, APIs, or telemetry platforms.
A collaborative approach to solving complex technical problems across different engineering disciplines.
Preferred Experience
Experience in one or more of the following areas would be beneficial, but you do not need to have experience in every area to be considered:
HPC environments and workflows, including technologies such as Slurm, Kubernetes, multi-node GPU workloads, or container platforms.
GPU profiling and performance analysis using ROCm, CUDA, or similar performance and tracing tools.
LLM training, fine-tuning, or evaluation, including approaches such as supervised fine-tuning or reinforcement learning.
GPU kernel development, tuning, or code generation using technologies such as Triton, CUDA, or HIP.
Performance optimization techniques including GEMM/MatMul optimization, operator fusion, mixed precision, and numerical validation.
Compiler and intermediate-representation technologies such as MLIR, LLVM, XLA, or related compiler frameworks.
Performance portability and optimization across different hardware architectures.
Developing or evaluating emerging agentic AI techniques and applying them to complex engineering workflows.
We recognize that strong candidates may bring different combinations of experience and expertise. If you are excited about the opportunity but do not meet every preferred qualification, we encourage you to apply. We value diverse perspectives, experiences, and approaches to problem-solving and are committed to creating an inclusive environment where people can contribute, develop, and succeed.
#LI-MH3
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.
