# Sr. AI/ML Platform Engineer Source: https://realanalystjobs.com/job/sr-ai-ml-platform-engineer-amd-69bee0ec Sr. AI/ML Platform Engineer at AMD, an on-site role based in Santa Clara, California, USA. Posted on 22 July 2026, read from AMD's own careers page (Jibe), which RealAnalystJobs re-checks daily and removes the role when it closes. AMD has approved H-1B petitions on USCIS record (employer-wide, not a promise for this role). Updated 15 November 2026. ## Key facts - Company: [AMD](https://realanalystjobs.com/companies/amd) - Location: Santa Clara, California, USA - Work mode: On-site - Posted: Jul 22, 2026 - Employment type: Full time - Source: AMD's careers page (Jibe), re-checked daily Skills: [Python](https://realanalystjobs.com/skills/python) ## Visa sponsorship The posting does not say. AMD had H-1B petitions approved for 568 workers in fiscal year 2026 (USCIS). That is history, not a promise for this role. [See the record](https://realanalystjobs.com/companies/amd#h1b-record) ## About this role 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 syste Description from AMD's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/sr-ai-ml-platform-engineer-amd-69bee0ec) and use Apply now there to reach the employer's application. The listing is re-checked against AMD's careers page daily and removed when it closes. ## AMD 20 open analyst roles on Real Analyst Jobs. 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