The role of ML Systems Research Engineer at AMD focuses on building reinforcement learning, inference, and evaluation infrastructure for AI-for-engineering systems. The team aims to enhance engineering workflows by developing scalable ML systems that facilitate model and agent improvement. Candidates will work on compute optimization, hardware engineering automation, and debugging to ensure practical and repeatable research outcomes. This position requires collaboration with scientists and applied engineers to create reliable and reproducible experiments.

• Strong programming skills in Python and experience with ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience building ML systems, RL infrastructure, or distributed experimentation systems.
• Strong understanding of model inference, batching, sampling, and reliability tradeoffs.
• Ability to design experiments and evaluation pipelines with clear metrics and reproducibility.
• Strong collaboration skills with AI researchers and applied engineers.

• Build RL and inference systems for engineering workflows, including job orchestration and experiment tracking.
• Develop infrastructure for long-horizon and high-latency reward tasks.
• Design staged rewards and uncertainty-aware evaluation methods.
• Support optimization workflows with systems for candidate generation and correctness checking.
• Partner with AI research scientists on reward shaping and post-training methods.
• Build scalable inference pipelines for LLM agents interacting with various tools.
• Standardize datasets and evaluation definitions for future training.
• Analyze experimental results to provide actionable guidance for improvements.

• AMD provides a competitive 'Total Rewards' package that focuses on financial growth, health, and work-life balance.