Job Description
You will join the Neural Network Compression Tools team within the OpenVINO Developer Tools organization.
As an AI Research Engineer/Scientist, you will drive the development of the Neural Network Compression Framework (NNCF).
Your responsibilities include research and development of state-of-the-art compression algorithms specifically 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 (with a focus on AI/Deep Learning).
Experience in Python programming and understanding of modern programming paradigms and patterns.
Proven experience in Deep Learning model optimization (specifically 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 experience.
Spoken and written English: upper-intermediate or advanced.
Preferred Qualifications
Hardware awareness: Understanding of how neural networks map to hardware (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 and Location
Experienced Hire
Shift: Shift 1 (Ireland)
Primary Location: Ireland, Leixlip
Non-Discrimination 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.
Compensation
Annual salary range: €76,****** – €120,******.
The compensation range displayed reflects the minimum and maximum target compensation.
Individual pay is determined by job related skills, experience, and relevant education, training.
As part of the total compensation approach, the final offer may include additional pay components and benefits.
Work Model
This role is eligible for a hybrid work model, allowing employees to split their time between working on-site at the 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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