Agent Systems

Advanced Micro Devices, Inc•Santa Clara, CA

3d•Hybrid

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology can change lives for the better. It can heal us, entertain us, and make us more connected, productive, and understanding of the world around us. And we’re looking for talent who feel the same: people who want to leave the planet better than they found it, those who don’t shy away from humanity’s challenges but are determined to help solve them. AMD is powering the next generation of supercomputing, high-performance computing, cloud, and AI. Whether you’re designing next-gen processors, enabling AI breakthroughs, or creating go-to-market plans, every role at AMD contributes to something bigger — technology that moves the world forward. THE ROLE We are hiring ML Systems Research Engineers to build the reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on the systems that let agents and models improve real engineering workflows: running many attempts, evaluating correctness, measuring performance, managing long-latency rewards, and feeding results back into model and agent improvement. You will work across compute optimization, hardware engineering automation, verification, simulation, debugging. The emphasis is on scalable ML systems that make research practical, repeatable, and useful for production engineering teams. THE PERSON You are a systems-minded ML engineer or researcher who understands that model quality depends on the surrounding loop: data, tools, inference, graders, reward design, logging, and iteration speed. You can build reliable infrastructure, reason about RL and inference tradeoffs, and collaborate with scientists and applied engineers to make experiments reproducible and useful.

Requirements

Strong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, TensorFlow, or similar.
Experience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems.
Strong understanding of model inference, batching, sampling, latency, throughput, observability, and reliability tradeoffs.
Ability to design experiments and evaluation pipelines with clear metrics, logs, reproducibility, and statistical discipline.
Strong collaboration skills with AI researchers, applied engineers, infrastructure engineers, and hardware domain experts.
Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience.

Nice To Haves

Experience with reinforcement learning, RLHF, GRPO, preference optimization, reward modeling, reward shaping, or post-training systems.
Experience with LLM agents, tool-use systems, code generation, automated program repair, compiler optimization, or benchmark-driven development.
Experience with distributed systems, job orchestration, Kubernetes, Ray, Slurm, workflow engines, data pipelines, or large-scale experiment management.
Familiarity with GPU systems, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference.
Exposure to hardware engineering workflows such as design, verification, firmware, simulation, or performance analysis is a strong plus.
Publications or shipped systems in ML systems, RL, inference optimization, AI infrastructure, or hardware/software co-design are valued.
Master's preferred; PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.

Responsibilities

Build RL and inference systems for agentic engineering workflows, including job orchestration, sampling, scoring, caching, experiment tracking, and reproducible evaluation.
Develop infrastructure for long-horizon and high-latency reward tasks where validation can take minutes to hours.
Design staged rewards, proxy graders, sliced evaluation paths, retry strategies, and uncertainty-aware evaluation methods.
Support optimization workflows with systems for candidate generation, benchmark execution, correctness checking, profiler feedback, reward modeling, and model-level improvement.
Partner with AI research scientists on reward hacking research, reward shaping, metareasoning, and post-training methods for engineering tasks.
Build scalable inference and tool-use pipelines for LLM agents that interact with compilers, profilers, simulators, formal tools, benchmark harnesses, and internal knowledge sources.
Standardize datasets, eval definitions, run logs, leaderboards, failure taxonomies, and data collection for future training.
Analyze experimental results and turn system behavior into actionable guidance for model, agent, tool, and reward improvements.