Machine Learning Engineer

Gotion, Inc.•Fremont, CA

5d•$110,000 - $135,000•Onsite

About The Position

Gotion Inc. is seeking a Machine Learning Engineer for its Product Development Team. The team designs and develops advanced battery products for next-generation energy storage system (ESS) and electric vehicle (EV) applications, leading the product development cycle and integrating mechanical, electrical, thermal, and control systems, along with system integration, validation, and certification. This fully on-site role is based in Fremont, California. The engineer will build machine learning models from the ground up and work with cross-disciplinary R&D teams to develop and deploy solutions for challenges involving advanced materials, electrochemical systems, and high-throughput data. The position calls for a strong background in model architecture design and algorithm development, ideally with experience in battery technology, energy systems, or related physical sciences. The US base salary range is $110,000 to $135,000, plus a 15% bonus and benefits. Pay within the range depends on work location and factors such as job-related skills, experience, and education or training.

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 machine learning libraries or frameworks such as PyTorch and TensorFlow.
Solid understanding of learning theory concepts, including regularization, generalization, loss functions, and evaluation metrics.
Excellent communication, collaboration, and problem-solving skills in interdisciplinary environments.

Nice To Haves

Experience with scientific or time-series datasets, especially in battery, materials, or energy domains.
A publication record in top-tier machine learning conferences such as NeurIPS, ICML, ICLR, or CVPR.

Responsibilities

Design and implement novel machine learning and deep learning models for internal research needs.
Prototype and evaluate algorithms, including Transformers, LLMs, and hybrid model architectures.
Conduct experimentation, benchmarking, and ablation studies.
Collaborate with battery scientists and domain experts to incorporate physical constraints or scientific priors into models.
Contribute to internal documentation and present research outcomes to technical and leadership teams.
Track and integrate advances from the machine learning research community.