Machine Learning Engineer - Gotion : Job Details

About the Role:

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 Fremont, CA, 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.

Key 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

Preferred Qualifications:• 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

• 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 in

interdisciplinary environments