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Ai research engineer/scientist (openvino, nncf)

Leixlip
Intel
Research engineer
Posted: 12 June
Offer description

Job Details
As an AI Research Engineer/Scientist, you will drive the development of the Neural Network Compression Framework (NNCF). Your responsibilities include researching and developing state‑of‑the‑art compression algorithms tailored for high‑performance neural network inference optimization within the OpenVINO ecosystem.
Qualifications

Master's or PhD in Computer Science, Mathematics, or a related field focused on AI/Deep Learning.
Experience in Python programming and modern programming paradigms and patterns.
Proven experience in deep learning model optimization (quantization, pruning, or sparsity).
Experience with model training frameworks such as PyTorch or TensorFlow and inference solutions such as OpenVINO.
At least 3 years of software development.
Spoken and written English: upper‑intermediate or advanced.
Hardware awareness: understanding of how neural networks map to CPU, GPU, NPU and why specific optimizations are necessary for memory‑constrained environments.
Familiarity with modern large language model architectures (transformers, diffusers, etc.).

Job Type
Experienced Hire
Shift
Shift 1 (Ireland)
Primary Location
Ireland, Leixlip
Posting Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Salary
Annual salary range: €76,900.00 - €120,100.00 (Ireland). Compensation may include additional pay components and benefits.
Work Model
This role is eligible for a hybrid work model, allowing employees to split time between working on‑site at their assigned Intel site and off‑site. Job posting details such as work model, location, or time type are subject to change.
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