Job description

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 hiring AI / ML Platform Engineers to build the platform layer that makes AI-for-engineering workflows scalable, reliable, and reproducible. This role focuses on the infrastructure and platform systems that support large-scale agent execution, distributed training and inference, experiment tracking, benchmark automation, artifact management, and GPU cluster utilization. You will work closely with ML Systems Research Engineers, AI Research Scientists, Applied AI Engineers, and hardware domain experts to operationalize the Blueprint framework across kernel optimization, RTL/PPA optimization, ECO fixing, verification, simulation, and debugging workflows. This is a platform engineering role, not a pure research role. The focus is to build robust shared systems that allow researchers and engineers to run more experiments, compare results reliably, reduce manual orchestration, and move successful workflows into production engineering use. THE PERSON: You are a strong systems engineer who enjoys building reliable platforms for AI researchers and applied engineers. You understand distributed systems, ML workloads, GPU infrastructure, experiment management, and production reliability. You can turn messy research workflows into reusable services, APIs, dashboards, job systems, and automation. You care about reproducibility, observability, performance, and developer experience. You are comfortable working across ML, infrastructure, and hardware/software tooling, and you can partner with research teams without requiring every requirement to be fully specified upfront. KEY RESPONSIBILITIES: • Build and operate the shared AI platform for agentic engineering workflows, including job submission, scheduling, orchestration, retries, logging, artifact storage, and experiment tracking. • Develop reliable infrastructure for distributed training, distributed inference, batch evaluation, and large-scale agent rollout across GPU clusters. • Build platform services for benchmark execution, correctness checking, profiling, regression tracking, and reproducible evaluation. • Maintain artifact systems for generated kernels, RTL edits, traces, logs, profiler outputs, benchmark results, simulator outputs, and formal verification artifacts. • Support scalable integrations with compilers, ROCm/HIP tooling, profilers, simulators, EDA tools, vLLM, SGLang, and internal engineering systems. • Improve GPU cluster utilization, scheduling efficiency, reliability, quota management, and workload isolation. • Build dashboards and observability systems for experiment status, resource usage, failure modes, benchmark trends, regression detection, and team productivity. • Partner with ML Systems Research Engineers to productionize research workflows for RL systems, inference systems, quantification systems, and evaluation pipelines. • Partner with Applied AI Engineers to make Blueprint harnesses reusable across kernel optimization, RTL optimization, verification, firmware, and CPU/GPU performance workflows. • Establish platform standards for reproducibility, data retention, run metadata, artifact lineage, access control, and operational reliability. TECHNICAL FOCUS AREAS: • Distributed ML platform infrastructure for training, inference, evaluation, and agent execution. • GPU cluster scheduling, utilization, reliability, quota management, and multi-user workload isolation. • Experiment tracking, artifact management, run lineage, dashboards, and reproducible workflow management. • Benchmark and evaluation automation for correctness, performance, regression detection, and reproducibility. • Integration with ROCm/HIP, Triton, compilers, profilers, vLLM, SGLang, simulators, formal tools, and EDA flows. • Caching and parallelization for expensive feedback loops, including simulator, compiler, verifier, and benchmark workloads. • Production-quality APIs, services, workflow engines, and developer tooling for research and engineering teams. PREFERRED QUALIFICATIONS: • Strong programming skills in Python and one or more systems languages such as C++, Go, or Rust. • Experience building ML platforms, AI infrastructure, distributed systems, workflow orchestration, experiment platforms, GPU cluster infrastructure, or developer platforms. • Strong understanding of job scheduling, distributed workloads, logging, monitoring, reliability, storage systems, and production operations. • Experience with Kubernetes, Ray, Slurm, workflow engines, containerization, CI/CD, data pipelines, or large-scale compute orchestration. • Ability to build reliable services, APIs, dashboards, and developer tools used by researchers and engineers. • Strong debugging skills across distributed systems, GPU workloads, storage systems, networking, containers, and production infrastructure. • Good collaboration skills with AI researchers, ML systems researchers, applied engineers and hardware domain experts. PREFERRED EXPERIENCE: • Experience with GPU platforms, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference. • Experience with vLLM, SGLang, Triton, PyTorch, JAX, Ray, Kubernetes, Slurm, MLflow, Weights & Biases, or similar systems. • Experience building experiment tracking systems, artifact stores, workflow engines, benchmark automation platforms, or developer productivity tools. • Experience supporting LLM agents, tool-use systems, large-scale sampling, or automated program optimization workflows. • Familiarity with compiler, profiler, simulator, formal verification, EDA, firmware, or hardware performance workflows is a strong plus. • Experience operating shared GPU clusters or high-performance ML infrastructure in a multi-user research environment. EDUCATION Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master's preferred; PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure. LOCATION : Santa Clara, CA #LI-BW1 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.

Requirements

Must-haves

Build and operate the shared AI platform for agentic engineering workflows, including job submission, scheduling, orchestration, retries, logging, artifact storage, and experiment tracking.
Develop reliable infrastructure for distributed training, distributed inference, batch evaluation, and large-scale agent rollout across GPU clusters.
Build platform services for benchmark execution, correctness checking, profiling, regression tracking, and reproducible evaluation.
Maintain artifact systems for generated kernels, RTL edits, traces, logs, profiler outputs, benchmark results, simulator outputs, and formal verification artifacts.
Support scalable integrations with compilers, ROCm/HIP tooling, profilers, simulators, EDA tools, vLLM, SGLang, and internal engineering systems.
Improve GPU cluster utilization, scheduling efficiency, reliability, quota management, and workload isolation.
Build dashboards and observability systems for experiment status, resource usage, failure modes, benchmark trends, regression detection, and team productivity.
Partner with ML Systems Research Engineers to productionize research workflows for RL systems, inference systems, quantification systems, and evaluation pipelines.
Partner with Applied AI Engineers to make Blueprint harnesses reusable across kernel optimization, RTL optimization, verification, firmware, and CPU/GPU performance workflows.
Establish platform standards for reproducibility, data retention, run metadata, artifact lineage, access control, and operational reliability.
Distributed ML platform infrastructure for training, inference, evaluation, and agent execution.
GPU cluster scheduling, utilization, reliability, quota management, and multi-user workload isolation.

Nice-to-haves

Strong programming skills in Python and one or more systems languages such as C++, Go, or Rust.
Experience building ML platforms, AI infrastructure, distributed systems, workflow orchestration, experiment platforms, GPU cluster infrastructure, or developer platforms.
Strong understanding of job scheduling, distributed workloads, logging, monitoring, reliability, storage systems, and production operations.
Experience with Kubernetes, Ray, Slurm, workflow engines, containerization, CI/CD, data pipelines, or large-scale compute orchestration.
Ability to build reliable services, APIs, dashboards, and developer tools used by researchers and engineers.
Strong debugging skills across distributed systems, GPU workloads, storage systems, networking, containers, and production infrastructure.
Good collaboration skills with AI researchers, ML systems researchers, applied engineers and hardware domain experts.
Experience with GPU platforms, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference.
Experience with vLLM, SGLang, Triton, PyTorch, JAX, Ray, Kubernetes, Slurm, MLflow, Weights & Biases, or similar systems.
Experience building experiment tracking systems, artifact stores, workflow engines, benchmark automation platforms, or developer productivity tools.
Experience supporting LLM agents, tool-use systems, large-scale sampling, or automated program optimization workflows.
Familiarity with compiler, profiler, simulator, formal verification, EDA, firmware, or hardware performance workflows is a strong plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythongorustkubernetesci/cdllmmachine learning

Platform: amd
Effort: medium