AMD is 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 collaborate 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 goal 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.

Key responsibilities include: 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 verific

AMD is 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 collaborate 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 goal 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.

Key responsibilities include: 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 verific