This role pays less than
72%
of similar roles. Most pay
$114,400–$241,750
— the shaded band above.
At the midpoint, this role pays about
$114k
versus about
$178k
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
474 open roles
on FindRole.
Listed pay typically runs
$166,400–$249,600
across 474 roles with salary data.
As a Summer 2027 PhD ML Systems Research Engineering Intern, you will join the team to develop and optimize infrastructure for distributed training, reinforcement learning, inference, and model optimization. You will build scalable systems for experiment management, rollout generation, model serving, and evaluation while improving efficiency through parallelism, scheduling, checkpointing, caching, and performance optimization. Your daily work involves developing tools that integrate AI models with engineering systems, evaluation pipelines, and benchmarking frameworks to support tracking, logging, monitoring, and reproducibility across machine learning workflows. You will collaborate with researchers to translate experimental requirements into production-quality infrastructure while analyzing system performance for reliability and resource utilization. The role requires proficiency in Python, experience with PyTorch or similar frameworks, and knowledge of computer systems, distributed systems, reinforcement learning, large language models, and AI infrastructure.
Develop and optimize infrastructure for distributed training, reinforcement learning, inference, and model optimization.
Build scalable systems for experiment management, rollout generation, model serving, and evaluation.
Improve training and inference efficiency through parallelism, scheduling, checkpointing, caching, and performance optimization.
Create tools to integrate AI models with engineering systems, evaluation pipelines, and benchmarking frameworks.
Support experiment tracking, logging, monitoring, and reproducibility across machine learning workflows.
Analyze system performance to identify improvements in reliability, scalability, and resource utilization.
Translate experimental requirements from researchers into production-quality infrastructure.
Document system designs, experimental results, and technical implementations.
What we're looking for
Must be currently enrolled in a PhD program in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related field.
Experience with Python programming.
Coursework or hands-on experience in artificial intelligence or computer systems.
Familiarity with PyTorch or similar machine learning frameworks.
Interest in distributed systems, reinforcement learning, LLMs, or AI infrastructure (preferred).
Strong analytical, problem-solving, and communication skills.
Machine Learning
Generative AI
Reinforcement Learning
LLM Post-training
RLHF
ML Systems
AI Agents
Reward Modeling
Preference Optimization
GPU Capacity Planning