Intel is seeking a Senior Machine Learning Engineer to work at the intersection of research and engineering on agent harness research, model fine-tuning, and post-training pipelines. The role involves designing and implementing algorithms for agent harness and post-training workflows, developing reinforcement learning environments and reward models, and running training experiments to enhance model capabilities for agentic applications. Responsibilities include building evaluation benchmarks and metrics, iterating on agent harness components (context engineering, memory, tools, and skills), maintaining the post-training pipeline from data ingestion to deployment, and designing RL environments and reward signals. You will debug and optimize training runs, profile GPU utilization, and address numerical instability at multi-GPU scale. Requirements include a BS in CS/EE/math or related STEM field, 8+ years software development, 4+ years ML/data science, proficiency in Python, and familiarity with LLM architectures and training dynamics. Preferred qualifications include an advanced degree, experience with end-to-end post-training pipelines for language models (supervised fine-tuning and reinforcement learning), and the ability to drive a research agenda independently. The role supports a hybrid work model across multiple U.S. locations (Santa Clara, Hillsboro, Phoenix, Folsom).

Intel is seeking a Senior Machine Learning Engineer to work at the intersection of research and engineering on agent harness research, model fine-tuning, and post-training pipelines. The role involves designing and implementing algorithms for agent harness and post-training workflows, developing reinforcement learning environments and reward models, and running training experiments to enhance model capabilities for agentic applications. Responsibilities include building evaluation benchmarks and metrics, iterating on agent harness components (context engineering, memory, tools, and skills), maintaining the post-training pipeline from data ingestion to deployment, and designing RL environments and reward signals. You will debug and optimize training runs, profile GPU utilization, and address numerical instability at multi-GPU scale. Requirements include a BS in CS/EE/math or related STEM field, 8+ years software development, 4+ years ML/data science, proficiency in Python, and familiarity with LLM architectures and training dynamics. Preferred qualifications include an advanced degree, experience with end-to-end post-training pipelines for language models (supervised fine-tuning and reinforcement learning), and the ability to drive a research agenda independently. The role supports a hybrid work model across multiple U.S. locations (Santa Clara, Hillsboro, Phoenix, Folsom).