below marketmost similar roles pay hereabove market
This role pays more than
85%
of similar roles. Most pay
$171,750–$249,000
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
About Amd
AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors
Amd currently has
496 open roles
on FindRole.
Listed pay typically runs
$172,000–$258,000
across 496 roles with salary data.
The Sr. AI/ML Platform Engineer joins the team to build a scalable platform layer for AI-for-engineering workflows. This role focuses on developing infrastructure for large-scale agent execution, distributed training, inference, experiment tracking, and GPU cluster utilization. You will build robust shared systems, APIs, and dashboards to turn research workflows into production services, supporting kernel optimization, RTL/PPA optimization, and verification. Key responsibilities include managing job scheduling, artifact storage, and benchmark automation. You will utilize technologies including Python, C++, Go, Rust, Kubernetes, Ray, Slurm, vLLM, SGLang, Triton, and ROCm/HIP. The role solves the technical challenge of operationalizing research into reliable engineering use by providing reproducible evaluation, performance profiling, and workload isolation across complex hardware and software tooling for ML systems and hardware domain experts.
Build dashboards and observability systems to monitor experiment status, resource usage, and failure modes.
Productionize research workflows for RL systems, inference systems, and evaluation pipelines.
What we're looking for
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience.
Master's degree (preferred); PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.
Strong programming skills in Python and one or more systems languages such as C++, Go, or Rust (preferred).
Experience building ML platforms, AI infrastructure, distributed systems, workflow orchestration, experiment platforms, GPU cluster infrastructure, or developer platforms (preferred).
Experience with GPU platforms, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference (preferred).
Experience with vLLM, SGLang, Triton, PyTorch, JAX, Ray, Kubernetes, Slurm, MLflow, Weights & Biases, or similar systems (preferred).
Experience supporting LLM agents, tool-use systems, large-scale sampling, or automated program optimization workflows (preferred).
Familiarity with compiler, profiler, simulator, formal verification, EDA, firmware, or hardware performance workflows is a strong plus (preferred).
Python
PyTorch
JAX
TensorFlow
Reinforcement Learning
LLM Agents
Kubernetes
Ray
Slurm
CUDA
ROCm
HIP
Distributed Systems
Data Pipelines
Model Serving
Profiling
Compilers
Simulators
Python
Azure ML SDK
Databricks
MLflow
Kubeflow
Docker
Kubernetes
CI/CD
Infrastructure as Code
Data Lake
Feature Store
LLM
Model Monitoring
ETL
Time-series Data
IoT
Mobile