Application Guide

How to Apply for Machine Learning Engineer

at Gotion

🏢 About Gotion

Gotion is a leader in electric vehicle and energy storage technology, specifically focused on advancing sustainable transportation and global green energy solutions. What makes Gotion unique is their integration of cutting-edge battery science with AI/ML innovation, creating a rare opportunity to apply machine learning to tangible physical systems with real-world environmental impact. Working here means contributing directly to decarbonization efforts through technological breakthroughs.

About This Role

This Machine Learning Engineer role involves designing and implementing novel ML/DL models specifically for battery research and energy storage optimization, requiring close collaboration with battery scientists to incorporate physical constraints into modeling. You'll be prototyping state-of-the-art algorithms like Transformers and LLMs, then conducting rigorous experimentation and benchmarking to advance Gotion's internal research capabilities. This position is impactful because your work directly influences the development of more efficient batteries and energy storage systems that power sustainable transportation.

💡 A Day in the Life

A typical day might involve collaborating with battery scientists to understand specific physical constraints for a new model, then prototyping a Transformer-based architecture in PyTorch to address those constraints. You'd likely spend time running experiments, analyzing results from ablation studies, and documenting findings for internal research teams, with regular check-ins to integrate the latest ML research advances into your work.

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🎯 Who Gotion Is Looking For

  • Has a Ph.D. or M.S. with demonstrated expertise in developing novel ML models (not just applying existing ones), with publications or projects showing algorithmic innovation
  • Possesses both theoretical understanding (regularization, generalization, loss functions) AND practical proficiency in PyTorch/TensorFlow for implementing complex architectures
  • Has experience or strong interest in applying ML to physical/engineering systems, with ability to collaborate effectively with domain experts like battery scientists
  • Demonstrates experience with rigorous experimentation methodologies including ablation studies and benchmarking, not just model deployment

📝 Tips for Applying to Gotion

1

Highlight specific experience with Transformers, LLMs, or hybrid architectures in your resume - Gotion explicitly mentions these as areas of focus

2

Include examples of collaborating with domain experts (like scientists or engineers) in previous roles, as this role requires working closely with battery scientists

3

Showcase any experience with physical systems or scientific applications of ML, even if not in batteries specifically

4

Demonstrate your ability to track and integrate ML research advances by mentioning recent papers or techniques you've implemented

5

Quantify your impact in previous ML research roles with metrics related to model performance improvements or research outcomes

✉️ What to Emphasize in Your Cover Letter

['Your experience with novel model development and algorithmic innovation, not just application of existing models', 'Specific examples of incorporating domain knowledge or physical constraints into ML models from previous projects', 'Your approach to rigorous experimentation and benchmarking methodologies', "Why you're specifically interested in applying ML to energy storage and sustainable transportation at Gotion"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Gotion's specific battery technologies and energy storage solutions - understand their technical differentiators
  • → Recent patents or publications from Gotion's research teams to understand their technical direction
  • → The intersection of ML with battery science - research how ML is currently applied in battery lifecycle prediction, material discovery, or optimization
  • → Gotion's partnerships and projects in the EV/sustainable energy ecosystem

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you approach incorporating physical battery constraints (like electrochemical properties) into a neural network architecture?
2 Walk through your process for designing and evaluating a novel ML model from conception to benchmarking
3 Discuss a recent ML research paper you found impactful and how you might apply its insights to battery optimization
4 Describe your experience with ablation studies and how you determine which model components are most critical
5 How have you previously collaborated with domain experts who don't have ML backgrounds?
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⚠️ Common Mistakes to Avoid

  • Focusing only on ML deployment experience without emphasizing research and novel model development
  • Presenting generic ML projects without demonstrating algorithmic innovation or rigorous experimentation
  • Failing to show interest in or understanding of the battery/energy storage domain that Gotion operates in

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Gotion!

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