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Battery Machine Learning Jobs in California (NOW HIRING)

Gotion, Inc.

Machine Learning Engineer

Fremont, CA

Collaborate with battery scientists and domain experts to incorporate physical constraints or ... D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied ...

Gotion, Inc.

Machine Learning Engineer

Fremont, CA · On-site

Gotion is powered by a leading power battery technology company that provides solutions for ... Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background ...

Sesame

Electrical Engineer

San Francisco, CA · On-site

$175K - $280K/yr

... areas: battery, RF, capacitive touch, audio, or image sensors. * Experience developing and shipping a wearable product, including machine perception or machine learning-powered features, with ...

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Battery Machine Learning information

What are the key skills and qualifications needed to thrive as a Battery Machine Learning Engineer, and why are they important?

To thrive as a Battery Machine Learning Engineer, you need a strong background in machine learning, data analysis, and battery science, typically supported by a degree in engineering, computer science, or a related field. Familiarity with Python, TensorFlow or PyTorch, data processing tools, and battery management system (BMS) software is highly valued. Strong problem-solving skills, collaboration, and effective communication set standout professionals apart in this role. These skills are essential to develop accurate predictive models that optimize battery performance and longevity, driving innovation in energy storage technologies.

What is battery machine learning and what do professionals in this field do?

Battery machine learning involves the application of machine learning algorithms to analyze, predict, and optimize the performance, lifespan, and safety of batteries. Professionals in this field work on developing data-driven models to forecast battery degradation, enhance energy management systems, and improve battery design. Their work is crucial in sectors such as electric vehicles, renewable energy storage, and consumer electronics, where battery efficiency and reliability are key. By leveraging large datasets from battery usage and testing, they help accelerate innovation and reduce costs in battery technology.

What are some common challenges faced by professionals working in Battery Machine Learning roles?

Professionals in Battery Machine Learning often encounter challenges related to limited or noisy datasets, as battery performance data can be expensive and time-consuming to collect. Additionally, integrating domain knowledge from electrochemistry with advanced machine learning techniques requires strong interdisciplinary collaboration. Staying up-to-date with both the latest AI methods and battery technology advancements is essential but can be demanding. Collaborating closely with researchers, engineers, and data scientists is a key aspect of the role, as projects frequently depend on cross-functional teamwork to translate predictive insights into practical battery innovations.
What are popular job titles related to Battery Machine Learning jobs in California? For Battery Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Battery Machine Learning jobs in California look for? The top searched job categories for Battery Machine Learning jobs in California are:
What cities in California are hiring for Battery Machine Learning jobs? Cities in California with the most Battery Machine Learning job openings:
Battery Machine Learning jobs near you
Infographic showing various Battery Machine Learning job openings in California as of July 2026, with employment types broken down into 94% Full Time, 4% Part Time, 1% Temporary, and 1% Contract. Highlights an 99% Physical, and 1% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Gotion, Inc.

Fremont, CA

Other

Re-posted 6 days ago

Job description

About The Team
The Product Development Team at Gotion Illinois New Energy Inc. focuses on the design and development of advanced battery products for next-generation energy storage system (ESS) and electric vehicle (EV) applications. We lead the full product development cycle, integrating mechanical, electrical, thermal, and control system to create high-performance battery solutions, along with comprehensive system integration, validation, and certification activities.

Role Summary

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.

Essential Duties and 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

Required 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

The US base salary range for this full-time position is $80,000.00 - $90,000.00 + 15% bonus + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.   

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