Foundational AI Research Scientist

Job Description Intel AI Lab is a research organization pursuing fundamental and applied research in Machine Learning. The problems we solve span computer vision, efficient ML architectures, chip design, computational chemistry and more. As a Research Scientist in Intel AI Lab, you will develop machine learning and optimization techniques to solve problems impacting semiconductor design and manufacturing. You will publish your findings, establish benchmarks, build open-source tools, and facilitate technology transfers. In this role, examples of your responsibilities are: Develop foundational machine learning capabilities for diverse applications. Develop machine learning techniques to accelerate optimization problems. Impact semiconductor design and manufacturing through research. Publish results in top-tier conferences and journals. Establish new industrial and academic benchmarks. Build open-source tools and facilitate technology transfers. Collaborate with teams to integrate research into commercial design flows. Qualifications You must possess the below minimum education requirements and minimum required qualifications to be initially considered for this position. Relevant experience can be obtained through schoolwork, classes, project work, internships, and/or military experience. Additional preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Minimum Qualifications: PHD with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or STEM-related field. 1+ years of experience and solid background in modern machine learning architectures: Transformers, Graph Neural Networks, Diffusion models, etc. 1+ years of experience and solid coding skills in Python and C++. 1+ years of experience and solid experience with machine learning frameworks like PyTorch. At least 1 first-author publication in top machine learning conferences like Neurips, ICML, ICLR etc. Preferred Qualifications: Prior work on deep learning on graphs (e.g., general graphs, circuit graphs, molecular graphs, or trees). Familiarity with classical search and optimization techniques like Genetic Algorithms, Monte-Carlo Tree Search, Djikstra’s Algorithm, etc. Experience with EDA tools, either commercial or open-source. Publications in top-tier venues in AI for chip design or AI for materials science.

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