ML Systems Research Engineer, RL / Inference / Agent Systems
Santa Clara, CA$204k - $306kFull-timePosted 1w ago
About the role
ML Systems Research Engineer focused on reinforcement learning, inference, and evaluation infrastructure for AI-for-engineering systems. Builds scalable ML systems enabling agents and models to improve real engineering workflows, with emphasis on long-horizon, high-latency tasks, reproducible experiments, and tool-use pipelines.
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.
Qualifications
- 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.
Skills
Required:PythonML frameworks (PyTorch, JAX, TensorFlow)Reinforcement Learning infrastructure
Nice to have:Distributed experimentation / job orchestration
Benefits
- Benefits offered are described: AMD benefits at a glance.
Last updated Oct 22, 2026. Interested? Apply on the company site.
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