ML Systems Research Engineer, RL / Inference / Agent Systems - CA, Santa Clara, United States

Originally posted 8 July 2026 by the employer. Relisted by the employer on 12 October 2026.

About the role

AMD is hiring ML Systems Research Engineers to build the reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on scalable ML systems across compute optimization, hardware engineering automation, verification, simulation, and debugging.

What you'll do

- 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, designing staged rewards, proxy graders, sliced evaluation paths, and retry strategies. - 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 interacting with compilers, profilers, simulators, formal tools, benchmark harnesses, and internal knowledge sources. - Standardize datasets, evaluation definitions, run logs, leaderboards, failure taxonomies, and data collection for future training.

What you'll need

- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. - Programming skills in Python and experience with ML frameworks such as PyTorch, JAX, or TensorFlow. - Experience building ML systems, RL infrastructure, inference services, agent frameworks, evaluation platforms, or distributed experimentation systems. - Understanding of model inference, batching, sampling, latency, throughput, observability, and reliability tradeoffs. - Experience designing experiments and evaluation pipelines with metrics, logs, reproducibility, and statistical discipline.

Nice to have

- Master's degree or PhD with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure. - 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.

Skills: reinforcement learning, inference infrastructure, evaluation infrastructure, scalable ML systems, LLM agents, GPU systems