About the Role:We are seeking a highly motivated Machine Learning Engineer with a strong background inmodel architecture design and algorithm development, ideally with experience in scientificdomains such as battery technology, energy systems, or related physical sciences. Thisis a fully on-site role based in Fremont, CA, focused on building innovative ML modelsfrom the ground up.You will collaborate closely with cross-disciplinary R&D teams to develop and deploymachine learning solutions that address real-world challenges in advanced materials,electrochemical systems, and high-throughput data environments.Key Responsibilities:• Design and implement novel machine learning and deep learning models tailored tointernal research needs• Prototype and evaluate state-of-the-art algorithms, including Transformers, LLMs,and hybrid model architectures• Conduct rigorous experimentation, benchmarking, and ablation studies• Collaborate with battery scientists and domain experts to incorporate physicalconstraints or scientific priors into modeling• Contribute to internal documentation and present research outcomes to technicaland leadership teams• Track and integrate advances from the ML research community to ensure technicalexcellencePreferred Qualifications:• Ph.D. (preferred) or M.S. in Machine Learning, Computer Science, ElectricalEngineering, Applied Mathematics, or a closely related field• Demonstrated expertise in model development, optimization, and algorithmicinnovation• Proficiency in Python and ML libraries/frameworks such as PyTorch, TensorFlow,etc.• Solid understanding of learning theory concepts such as regularization,generalization, loss functions, and evaluation metrics• Experience working with scientific or time-series datasets, especially in battery,materials, or energy domains, is highly desirable• A publication record in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR)is a strong plus• Excellent communication, collaboration, and problem-solving skills ininterdisciplinary environments #J-18808-Ljbffr