Machine Learning Engineer

Gotion, Inc.•Fremont, CA

7d•$80,000 - $90,000•Onsite

About The Position

Gotion Inc. is 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.

Requirements

Ph.D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field
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 Haves

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

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