Neuromorphic Applications Researcher- Temporary Position
Intel · Guadalajara, Mexico
About this role
Intel is hiring a mid-level AI Research Scientist in the machine learning function based in Guadalajara, Mexico. The posting calls out experience with Python, TensorFlow, PyTorch, LLMs.
- Role
- AI Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Guadalajara, Mexico
- Posted
- May 19, 2026
More roles at Intel
Job description
from Intel careersJob Description:
This position requires candidates to upload a resume in English; you are welcome to upload multiple versions of your resume if you prefer but an English version of your resume will be required to be considered for this position.
For nearly a decade, Intel's Neuromorphic Computing Lab-together with a global ecosystem of 250+ research groups-has explored architectures, algorithms, and software inspired by the brain's extraordinary efficiency, scalability, and adaptability. Our Loihi series of research chips pioneered event-driven, sparse, and massively parallel neuro-inspired processing, fueling over 100 peer-reviewed publications that validate its promise.
Now, we're entering an exciting new chapter: transforming these breakthroughs into real-world products that will power the coming era of physical AI systems-beyond the reach of GPUs and mainstream AI accelerators.
If you are passionate about pushing the boundaries of computing, from transistor-level innovation to software abstractions, join us. Help define the next wave of AI technology that harnesses the proven advantages of Intel's neuromorphic computing technology with the versatility demanded by modern AI workloads.
Position Overview
Demonstrate the value of Intel's neuromorphic technologies by developing, implementing, and benchmarking algorithms for Intel's next-generation neuromorphic architecture to enable applications in edge computing, signal processing, and autonomous systems for the era of physical AI with groundbreaking performance and efficiency.