below marketmost similar roles pay hereabove market
This role pays more than
93%
of similar roles. Most pay
$162,000–$230,425
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$196k
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 Applied AI Engineer joins the hardware and software engineering teams to develop AI-assisted systems for high-priority engineering workflows. This role involves converting manual processes into structured tasks involving candidate generation, validation, scoring, and logging to improve metrics like correctness, latency, and engineer time saved. You will build agentic systems for code generation, debugging, and root-cause analysis while developing tools that connect LLMs to compilers, simulators, and formal checks. Key responsibilities include building human-in-the-loop workflows and improving model performance through better prompts and retrieval. Required skills include software engineering in Python and systems languages like C++, C, HIP, CUDA, or Rust. The work focuses on hardware-adjacent domains including design optimization, verification, simulation, firmware, performance debugging, routing, and compute library optimization.
Build applied AI workflows for hardware and software priorities like optimization, verification, and debugging.
Convert manual engineering processes into structured tasks with candidate generation, validation, and scoring.
Develop tools that allow AI agents to interact with compilers, simulators, profilers, and ticket systems.
Build human-in-the-loop workflows for tasks requiring expert judgment or subjective triage.
Improve model and agent performance through prompt engineering, retrieval, evaluation datasets, and structured feedback.
Generalize repeated patterns into reusable platforms, dashboards, and data assets with research and infrastructure teams.
Communicate progress through measurable outcomes, technical writeups, and stakeholder updates.
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 for candidates with relevant AI, systems, EDA, or hardware/software co-design experience.
Strong software engineering experience in Python and at least one systems language such as C++, C, HIP, CUDA, or Rust (preferred).
Experience building applied AI, ML, agentic, automation, or developer tooling systems for technical users (preferred).
Experience with hardware design or verification workflows, including RTL, Verilog/SystemVerilog, simulation, formal verification, EDA tools, timing analysis, or power/performance/area tradeoffs (preferred).
Experience with GPU/CPU performance engineering, compiler tooling, profilers, kernel optimization, ROCm/HIP, CUDA, or benchmarking (preferred).
Familiarity with LLM agents, tool use, retrieval, LLM-as-judge workflows, RL, or post-training methods (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
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