below marketmost similar roles pay hereabove market
This role pays more than
87%
of similar roles. Most pay
$154,962–$235,000
— the blue band above.
At the midpoint, this role pays about
$255k
versus about
$195k
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. Forward Deployed AI Engineer joins the team to build, evaluate, and deploy production-ready AI systems that solve complex engineering challenges for enterprise customers. This role involves transforming ambiguous technical requirements into scalable solutions by designing LLM agents, engineering automation, and data pipelines. You will work directly with customers and researchers to develop post-training solutions, including supervised fine-tuning, reinforcement learning, preference optimization, and reward modeling. Key technical requirements include proficiency in Python and systems languages like C++, Rust, C, TypeScript, CUDA, or HIP. You must possess deep knowledge of transformer-based LLMs, inference, and agent architectures. The role requires utilizing tools such as PyTorch, Hugging Face, JAX, TensorFlow, Ray, and vLLM to deliver measurable business impact through high-performance computing and distributed AI infrastructure.
Design, build, and deploy production-grade AI applications, LLM agents, and engineering automation solutions.
Develop evaluation frameworks, benchmarks, and data pipelines to measure model quality and business impact.
Build and evaluate LLM post-training solutions including supervised fine-tuning, reinforcement learning, and reward modeling.
Diagnose and optimize AI system performance across models, data, infrastructure, orchestration, and tooling.
Partner with customer engineering teams to translate ambiguous technical requirements into scalable AI solutions.
Transform recurring customer use cases into reusable AI infrastructure and platform capabilities.
Present technical solutions and implementation strategies to engineering leaders, customers, and executive stakeholders.
What we're looking for
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or a related field (or equivalent practical experience).
Master's degree (preferred); PhD is a plus for candidates with strong applied research or systems experience.
Strong software engineering experience in Python and at least one systems language such as C++, Rust, C, TypeScript, CUDA, or HIP.
Proven experience building production AI or machine learning systems beyond prompt engineering or API integrations.
Hands-on experience with LLM post-training, Reinforcement Learning, Preference Optimization, Reward Modeling, Model/Agent Evaluation, or AI Training/Inference Infrastructure.
Deep understanding of transformer-based LLMs, inference, fine-tuning, retrieval, evaluation, and agent architectures.
Experience designing reproducible evaluation frameworks using benchmarks, testing, and measurable performance metrics.
Experience with PyTorch, Hugging Face, JAX, TensorFlow, Ray, vLLM, or distributed AI infrastructure (preferred).
Python
PyTorch
JAX
TensorFlow
Reinforcement Learning
LLM Agents
Kubernetes
Ray
Slurm
CUDA
ROCm
HIP
Distributed Systems
Data Pipelines
Model Serving
Profiling
Compilers
Simulators
Large Language Models
AI Platform
Distributed Systems
Workflow Systems
Context Engineering
Human-in-the-loop
Internal Developer Platform
Platform Engineering