Sr. AI/ML Platform Engineer

Job Title

Sr. AI/ML Platform Engineer

Role Summary

Build and operate the shared AI/ML platform that enables large-scale agent execution, distributed training and inference, experiment tracking, artifact management, and GPU cluster utilization for engineering workflows.

Work with ML systems researchers, applied AI engineers, and hardware domain experts to productionize research workflows and convert experimental pipelines into reliable, reproducible platform services and developer tools.

Experience Level

Senior. Typically a senior-level role (commonly 7+ years of relevant experience) focused on platform and infrastructure engineering for ML workloads.

Responsibilities

Primary responsibilities include building, operating, and improving platform services and tooling for ML research and engineering workflows.

• Design, implement, and operate job submission, scheduling, orchestration, retries, logging, and artifact storage for large-scale experiments.
• Develop infrastructure for distributed training, distributed inference, batch evaluation, and agent rollout across GPU clusters.
• Build benchmark execution, correctness checks, profiling, regression tracking, and reproducible evaluation pipelines.
• Manage artifact systems for kernels, RTL edits, traces, logs, profiler outputs, benchmark results, simulator outputs, and verification artifacts.
• Integrate with compilers, ROCm/HIP tooling, profilers, simulators, EDA tools, model tooling, and internal engineering systems.
• Improve GPU cluster utilization, scheduling efficiency, quota management, and workload isolation.
• Create dashboards and observability for experiment status, resource usage, failure modes, and regression detection.
• Partner with research and applied teams to productionize workflows and establish platform standards for reproducibility, metadata, artifact lineage, and access control.

Requirements

Must-have technical skills and experience to succeed in this role; preferred items are listed separately.

• Proven experience building or operating ML platform infrastructure, experiment tracking, or artifact management systems for research or engineering teams.
• Solid understanding of distributed systems, job scheduling, cluster orchestration, and multi-user GPU environments.
• Experience with production reliability concerns: logging, monitoring, storage, quota management, and workload isolation.
• Ability to design and ship production-quality APIs, services, workflow engines, and developer tooling that support reproducible workflows.
• Strong debugging skills across distributed systems, GPU workloads, storage, networking, containers, and production operations.
• Good collaboration skills to work with ML researchers, applied engineers, and hardware experts.
• Nice to have: experience with Python plus a systems language (C++, Go, Rust); Kubernetes, Ray, Slurm, Triton, vLLM, SGLang, MLflow, Weights & Biases; ROCm/HIP or CUDA; profiler and benchmarking tooling.

Education Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related technical field, or equivalent practical experience. Master's degree preferred; PhD is a plus, particularly for backgrounds in ML systems, distributed systems, GPU computing, or AI infrastructure.

About the Company

Company: Advanced Micro Devices

Headquarters: Sunnyvale, California, USA

Advanced Micro Devices, or AMD, is a global semiconductor company that designs and manufactures microprocessors, graphics processors, and related technologies for a variety of computing devices. Known for pushing the boundaries of innovation, AMD's mission is to deliver high-performance computing solutions for AI, data centers, gaming, and embedded applications. They foster a collaborative, inclusive culture focused on creativity and problem-solving, aiming to drive progress and excellence in technology.