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
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