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Strong foundation in mathematics: linear algebra, probability, stochastics, optimization theory
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Expertise in mathematical programming, algorithm design, and optimization techniques
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Skilled in formulating complex problems and designing scalable algorithms (e.g. linear/non-linear programming, convex optimization, combinatorial algorithms, etc.) and experience improving algorithms for efficiency and scalability
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Deep knowledge of machine learning, deep learning, and statistical modeling
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Strong in Graph Theory or Knowledge Graph related architecture and database (e.g. Neo4j, cuGraph)
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Hands-on experience with neural networks, transformers, diffusion models, or generative modeling
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Familiarity with NLP, computer vision, or domain-specific AI applications
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Proficient in model evaluation, validation, and performance metrics
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Experience with AI/ML frameworks and libraries (e.g. TensorFlow, PyTorch)
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Familiarity with software development practices (version control, testing, GPU accelerated computing)
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Strong analytical thinking and problem-solving skills
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Ability to derive insights and prove algorithmic effectiveness through rigorous logic and math
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Demonstrated creativity in tackling open-ended research and real-world AI challenges
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Clear communicator, able to translate complex ideas for technical and non-technical audiences
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Effective collaborator in cross-functional teams with researchers, engineers, and business partners
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Skilled in writing technical documentation, reports, and academic publications is a plus
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Passion for AI advancement and continuous learning
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Active interest in emerging AI research, with contributions to publications, open-source projects or conference preferred