Senior AI/ML Platform Engineer — Scalable GPU Pipelines

Advanced Micro Devices, Inc.; Santa Clara (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Advanced Micro Devices, Inc. (AMD) in Santa Clara, CA, is hiring AI/ML Platform Engineers to build a robust platform layer for scalable AI-for-engineering workflows.

You will design infrastructure supporting large-scale agent execution, distributed training and inference, experiment tracking, benchmarking, and artifact management. This is a platform engineering role focused on production-ready shared systems for researchers and engineers.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field. Master's preferred; PhD a plus.

Responsabilités

• 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.

Connaissances

Python

C++

Go

Rust

Kubernetes

Ray

Slurm

CI/CD

APIs

Formation

Bachelor's degree in CS/Engineering

Outils

Triton

PyTorch

CUDA

MLflow

Weights & Biases

vLLM

SGLang

Description du poste

Advanced Micro Devices, Inc. (AMD) in Santa Clara, CA, is hiring AI/ML Platform Engineers to build a robust platform layer for scalable AI-for-engineering workflows.

You will design infrastructure supporting large-scale agent execution, distributed training and inference, experiment tracking, benchmarking, and artifact management. This is a platform engineering role focused on production-ready shared systems for researchers and engineers.