Application Guide

How to Apply for Machine Learning Scientist, AI Explainability

at SES

🏢 About SES

SES is pioneering AI-driven sustainable technology by developing software systems that enhance eco-friendly Lithium-Metal battery production. This unique intersection of AI research and tangible environmental impact makes it an exciting workplace for scientists who want their ML work to directly address climate challenges. The company operates at the cutting edge where AI explainability meets materials science innovation.

About This Role

This Machine Learning Scientist role focuses specifically on AI Explainability within the context of battery and material discovery using multimodal Large Language Models. You'll lead research into how LLMs approach problem-solving for basic battery design questions while integrating domain-specific scientific data into model training. Your work will directly influence how AI agents can accelerate sustainable battery development through interpretable AI systems.

💡 A Day in the Life

Your typical day involves designing experiments to probe how multimodal LLMs reason about battery chemistry problems, analyzing model attention patterns across scientific documents and experimental data, and collaborating with materials scientists to validate AI-generated insights. You'll spend significant time optimizing training pipelines that combine proprietary battery test results with public research while developing visualization tools to make LLM decision-making transparent to domain experts.

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Interview Prep

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🎯 Who SES Is Looking For

  • Holds an advanced degree (MS/PhD) in fields like Computational Neuroscience or Cognitive Science with published research on LLM interpretability or mechanistic interpretability
  • Has hands-on experience troubleshooting LLM training pipelines, specifically addressing data quality issues with scientific or technical documents
  • Demonstrates experience with multimodal LLMs applied to scientific domains, preferably with materials science or chemistry applications
  • Can articulate specific examples of researching how LLMs approach planning and solution generation in constrained problem spaces

📝 Tips for Applying to SES

1

Highlight any experience with scientific document processing for LLMs - specifically mention techniques for handling materials science literature or technical papers

2

Prepare a portfolio showing LLM interpretability research, especially if it involves analyzing model reasoning processes in technical domains

3

Demonstrate understanding of battery chemistry basics in your application materials to show domain readiness

4

Include specific examples of optimizing LLM training for efficiency when working with heterogeneous data sources

5

Reference SES's specific mission in your application materials, showing how your AI explainability work aligns with sustainable battery development

✉️ What to Emphasize in Your Cover Letter

['Your experience with mechanistic interpretability of LLMs and how it applies to scientific problem-solving', 'Specific examples of integrating domain knowledge (especially from technical documents) into LLM training pipelines', 'How your research approach to AI explainability can accelerate battery materials discovery', 'Your perspective on making LLM reasoning transparent for scientific validation in high-stakes applications']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → SES's specific Li-Metal battery technology and their published research on AI-assisted materials discovery
  • → Current challenges in battery materials science where LLM-based approaches could provide breakthroughs
  • → The company's existing AI publications or conference presentations on explainable AI
  • → Competitive landscape in AI-driven battery development and SES's unique positioning

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through your approach to making an LLM's problem-solving process interpretable for battery design questions
2 How would you handle integrating proprietary battery test data with public scientific literature in a multimodal LLM?
3 Describe a time you optimized LLM training when dealing with noisy or incomplete scientific data
4 What metrics would you use to evaluate the explainability of an AI agent solving materials discovery problems?
5 How would you design experiments to understand how LLMs approach planning in constrained chemical search spaces?
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⚠️ Common Mistakes to Avoid

  • Presenting generic LLM experience without specific examples of working with scientific/technical domains
  • Focusing only on model performance metrics without addressing explainability requirements for scientific validation
  • Failing to demonstrate understanding of why explainability is critical for AI applications in materials science and battery development

📅 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

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