Machine Learning Engineer

We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains such as battery technology, energy systems, or related physical sciences. This is a fully on-site role based in Manteno, IL focused on building innovative ML models from the ground up. You will collaborate closely with cross-disciplinary R&D teams to develop and deploy machine learning solutions that address real-world challenges in advanced materials, electrochemical systems, and high-throughput data environments.

Responsibilities

Design and implement novel machine learning and deep learning models tailored to internal 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 physical constraints or scientific priors into modeling; Contribute to internal documentation and present research outcomes to technical and leadership teams; Track and integrate advances from the ML research community to ensure technical excellence

Requirements

M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field (Ph.D. preferred); Demonstrated expertise in model development, optimization, and algorithmic innovation; 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; Excellent communication, collaboration, and problem-solving skills in interdisciplinary environments

Nice to have

Ph.D. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field; Experience in scientific domains such as battery technology, energy systems, or related physical sciences; Experience working with scientific or time-series datasets, especially in battery, materials, or energy domains; Publication record in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR)

Benefits

15% bonus; benefits