Senior ML Engineer: Edge AI & Agentic RL

Intel; Phoenix (AZ); Hybrid; USD 195,200 - 361,200; Full time

Job summary

Intel in the United States is seeking a Machine Learning Engineer / Data Scientist to join our team, working on agent harness research and model fine tuning.

The role sits at the intersection of research and engineering: the ideal candidate designs and implements algorithms for agent harness and post-training pipelines, develops RL environments and reward models, and conducts training runs to improve model capabilities for agentic applications.

Qualifications

• BS in CS, EE, Math or related STEM field.
• 8+ years software development background.
• 4+ years of hands-on experience in machine learning engineering, data science or ML research.
• Experiences designing and building evaluation frameworks and benchmarks that accurately measure model capability improvements and alignment quality
• Proficient in Python
• Proficient in LLM architectures, optimization and model training dynamics.

Responsibilities

• Build evaluation benchmarks and metrics
• Build and iterate on agent harness, including context engineering, agent memory, tools, skills.
• Build, maintain, and iterate on the post-training pipeline: Develop robust, reproducible training workflows from data ingestion and preprocessing through model checkpointing and deployment
• Design RL environments and reward functions - Develop environments, reward signals, and verifiable reward frameworks for training models on reasoning-intensive tasks.
• Debug and optimize training runs - Profile training jobs, resolve bottlenecks, improve GPU utilization, and address numerical instability at multi-GPU scale

Skills

Python

LLM architectures

ML research

Evaluation benchmarks

Cross-functional collaboration

Education

BS in CS, EE, Math or related STEM field

Job description

Intel in the United States is seeking a Machine Learning Engineer / Data Scientist to join our team, working on agent harness research and model fine tuning.

The role sits at the intersection of research and engineering: the ideal candidate designs and implements algorithms for agent harness and post-training pipelines, develops RL environments and reward models, and conducts training runs to improve model capabilities for agentic applications.