RL & Inference ML Systems Engineer for Engineering AI

AMD; Santa Clara (CA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

AMD benefits

Job summary

AMD is hiring ML Systems Research Engineers to build reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on scalable ML systems that support agents and models improving real engineering workflows, with tasks like running many attempts and measuring performance.

You will collaborate with scientists and engineers across compute optimization, verification, and tooling to make experiments reproducible and useful for production teams.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. Master’s preferred; PhD is a plus, especially with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure.
• 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.
• 1Strong collaboration skills with AI researchers, applied engineers, infrastructure engineers, and hardware domain experts.

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.

Skills

Python programming

ML frameworks (PyTorch/JAX/TensorFlow)

RL infrastructure

Distributed experimentation

Observability and reliability

Experiment design and statistics

Collaboration with researchers/enginee

Education

Bachelor’s degree in CS/CE/EE/ML, or related; Master’s preferred

Tools

Kubernetes

Ray

Slurm

Workflow engines

Data pipelines

GPU profiling tools

Job description

AMD is hiring ML Systems Research Engineers to build reinforcement learning, inference, and evaluation infrastructure behind AI-for-engineering systems. This role focuses on scalable ML systems that support agents and models improving real engineering workflows, with tasks like running many attempts and measuring performance.

You will collaborate with scientists and engineers across compute optimization, verification, and tooling to make experiments reproducible and useful for production teams.