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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. AI/ML Platform Engineer - **Company:** Advanced Micro Devices, Inc. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, C++ (Programming Language), Computer Clusters, Compilers, Software Code Optimization, Profiling, Nvidia CUDA, Computer Programming, Computer Engineering, Continuous Integration, Data Retention, Software Debugging, Programming Tools, Distributed Computing Environment, Distributed Systems, Electronic Design Automation, Firmware, Formal Verification, General-Purpose Computing on Graphics Processing Units, Job Scheduling, Python (Programming Language), Machine Learning, Productivity Software, Azure Machine Learning, Workflow Management Systems, AI Infrastructure, Reinforcement Learning, Data Logging, Pytorch, Evaluation Pipelines, Large Language Models, Caching, Distributed Inference, Containerization, AI Platforms, Kubernetes, Information Technology, SGLang, ROCm, Slurm, Machine Learning Operations, VLLM, Data Pipelines - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25197_c9a36b1adaf80bd6767f3820b87097c8 ## About the Role * 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. * Experience operating shared GPU clusters or high-performance ML infrastructure in a multi-user research environment., 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. ## Description 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., * 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. * 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., AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.