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At Intel, our journey is to transform AI into something safer, more trustworthy, and respectful of human privacy by design. We believe transformative AI should have a positive impact on people-powerful in capability, yet honest about its limits and protective of the data and resources it touches.

To get there, we build agentic AI that combines the best of local and cloud intelligence - private, affordable, and sustainable by design. Small, efficient models run directly on the user's machine (AI PC, edge, on-prem, and beyond), keeping data private and token costs low, while powerful cloud models handle the hardest work: planning, reasoning, and complex problem-solving. Today, neither approach can deliver this alone. Together, they give people real capability without compromise-data stays private, spend stays predictable, and energy use stays in check.

We're building intelligence that scales without sacrificing trust, cost, or the planet-because the future of AI should belong to the people it serves

We are seeking a **Machine Learning Engineer / Data Scientist** to join our team, working on agent harness research and model fine tuning. This 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.

Work in a dynamic team to:

• 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

Curiosity is required. You will develop:

• How post-training techniques actually move model performance

• How to make small models punch above their weight as agent backends

• How model choices interact with runtime constraints on edge hardware

IMPORTANT:

Please be informed that Intel is proactively trying

to find candidates for this position which is frequently available

at Intel.

Please note that the position may not be available

at this time. If you would be interested in this position should it

become available, we would encourage you to apply, and our

hiring team will be glad to contact you when/if relevant.