below marketmost similar roles pay hereabove market
This role pays more than
87%
of similar roles. Most pay
$194,887–$254,750
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$225k
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 ML Systems Research Engineer joins the team to build reinforcement learning, inference, and evaluation infrastructure for AI-for-engineering systems. This role focuses on creating scalable systems that allow agents and models to improve engineering workflows by managing long-latency rewards, measuring performance, and ensuring reproducible evaluations. You will build job orchestration, sampling, scoring, and caching systems while designing staged rewards and proxy graders. Key responsibilities include developing tool-use pipelines for LLM agents that interact with compilers, profilers, and simulators. The role requires proficiency in Python and ML frameworks like PyTorch, JAX, or TensorFlow. You will work on distributed experimentation, Kubernetes, Ray, and Slurm to solve technical problems in reward shaping, post-training methods, and hardware engineering automation for production engineering teams.
Build scalable inference and tool-use pipelines for LLM agents interacting with compilers, profilers, and simulators.
Standardize datasets, evaluation definitions, run logs, leaderboards, and failure taxonomies for future training.
Analyze experimental results to provide actionable guidance for model, agent, tool, and reward improvements.
Partner with research scientists on reward shaping, metareasoning, and post-training methods for engineering tasks.
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).
Strong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, or TensorFlow (preferred).
Experience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems (preferred).
Experience with reinforcement learning, RLHF, GRPO, preference optimization, reward modeling, reward shaping, or post-training systems (preferred).
Experience with LLM agents, tool-use systems, code generation, automated program repair, compiler optimization, or benchmark-driven development (preferred).
Experience with distributed systems, job orchestration, Kubernetes, Ray, Slurm, workflow engines, data pipelines, or large-scale experiment management (preferred).
Python
C++
Rust
C
TypeScript
CUDA
HIP
PyTorch
Hugging Face
JAX
TensorFlow
Ray
vLLM
LLM Post-training
Reinforcement Learning
RLHF
PPO
DPO
GRPO
Preference Optimization
Reward Modeling
Distributed Systems
High-Performance Computing
Data Pipelines
Reinforcement Learning
Python
PyTorch
TensorFlow
JAX
Operations Research
Digital Twins
Simulation
Combinatorial Optimization
Sequential Decision-Making
Neural Optimization
AWS
Azure
GCP
Stochastic Systems
Control Theory
World Models
AI-OR