We are seeking a Machine Learning Scientist with expertise in designing state-space models, developing novel machine learning architectures, and contributing to groundbreaking research. The ideal candidate will have a strong foundation in classical machine learning, deep learning, and extensive coding experience. A background in writing government grant proposals and proficiency in CI/CD pipelines is a plus. The target domains are custom, extreme-edge AI processors. Extreme-Edge are system that are high performance but run on 250 mw of power or less. Key Responsibilities: Design and develop state-space models and novel machine learning architectures. Conduct research to advance the state of the art in machine learning and publish findings in leading journals or conferences. Build, train, and deploy machine learning models for real-world applications. Collaborate with cross-functional teams to integrate ML solutions into production systems. Perform data preprocessing, and hyperparameter tuning for optimal model performance. Develop and maintain CI/CD pipelines for machine learning workflows. Stay updated with the latest trends in AI/ML and apply innovative techniques to solve complex problems. Generate Patents, publish in high impact journals and conferences. Required Qualifications: Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field. Proven experience with state-space models and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong coding skills in Python and C/C++ (bonus). Solid understanding of classical machine learning techniques, algorithms, and statistical methods. Demonstrated record of publications in reputable journals or conferences. Active GitHub repository showcasing relevant projects or contributions. Highly creative and curious. Preferred Skills: Experience writing and winning government grants and contracts. Familiarity with CI/CD tools, GitLab CI/CD, or similar platforms. Strong problem-solving skills with attention to detail. Excellent communication skills for presenting technical concepts to non-experts.