Gotion, Inc.
Machine Learning Engineer
About this role
Gotion seeks a Machine Learning Engineer to develop innovative ML models for battery and energy storage systems. You'll work on-site in Manteno, IL, collaborating with R&D teams to build cutting-edge algorithms that solve real-world challenges in advanced materials and electrochemical systems.
What you'll do
- Design and implement novel machine learning and deep learning models for internal research applications
- Prototype and evaluate advanced algorithms including Transformers, LLMs, and hybrid architectures
- Conduct experimentation, benchmarking, and ablation studies to validate model performance
- Partner with battery scientists to integrate physical constraints and domain knowledge into models
- Document research and present findings to technical and leadership stakeholders
- Monitor ML research community advances to maintain technical excellence
What they're looking for
- Machine learning model architecture and design
- Python and deep learning frameworks (PyTorch, TensorFlow)
- Learning theory (regularization, generalization, loss functions, evaluation metrics)
- Scientific and time-series data analysis
- Algorithm optimization and innovation
- Cross-functional collaboration and communication
- Transformers and large language models
- Battery or energy domain knowledge (preferred)
Benefits
- Base salary $80,000–$90,000 plus 15% bonus
- Full benefits package
- On-site role with collaborative R&D environment
- Opportunity to work on next-generation battery and EV technology
- Access to global R&D centers across multiple continents
- Equal opportunity employer with inclusive workplace culture
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Gotion, Inc.
Gotion, Inc. manufactures large-scale lithium battery cells and packs for electric vehicles and energy storage systems, with a major 40GW facility under construction in Illinois. The company is hiring mechanical, electrical, thermal, and systems engineers to design and commission manufacturing infrastructure, quality control systems, and battery thermal performance.
- Website
- gotion.com
Likely interview questions
- Can you walk us through a machine learning project where you designed a novel model architecture from scratch? What was your process for iterating and validating your approach?
- Tell us about your experience working with time-series or scientific datasets. How did you handle domain-specific constraints or incorporate physical priors into your models?