Application Guide

How to Apply for Staff Software Engineer, Model LifeCycle

at Crusoe

🏢 About Crusoe

Crusoe uniquely transforms stranded energy (like flared natural gas) into eco-friendly power for data centers, significantly reducing environmental impact. Their mission combines cutting-edge AI with sustainable energy solutions, making them attractive to engineers who want their technical work to have positive environmental consequences. Working here means contributing to both AI advancement and climate innovation simultaneously.

About This Role

This Staff Software Engineer role focuses on the entire lifecycle of large foundation models—from fine-tuning systems (SFT, PEFT, LoRA) to multi-node orchestration, checkpointing, and cost-efficient scaling. You'll build end-to-end training pipelines for LLMs, develop agent execution infrastructure, and implement features for dataset/model/experiment management at scale. This role is impactful because you'll directly enable efficient, reproducible AI development while supporting Crusoe's mission of sustainable computing.

💡 A Day in the Life

A typical day involves designing and implementing features for fine-tuning systems using techniques like LoRA and adapters, optimizing multi-node training orchestration for better failure recovery, and collaborating on distillation pipelines for preference optimization. You'll spend time improving dataset management systems for versioning and lineage tracking while ensuring training pipelines remain cost-efficient and scalable, all within the context of Crusoe's sustainable computing infrastructure.

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Interview Prep

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🎯 Who Crusoe Is Looking For

  • Has 8-10+ years building production-level services in Golang or Python, with proven experience leading initiatives in AI infrastructure
  • Deep hands-on experience with PyTorch, training/fine-tuning LLMs (including techniques like SFT, PEFT, LoRA), and performance optimization for large-scale systems
  • Experience with multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling of model training pipelines
  • Background in implementing distillation/RL pipelines (preference optimization, policy optimization) and developing agent execution infrastructure

📝 Tips for Applying to Crusoe

1

Highlight specific examples of multi-node orchestration and checkpointing systems you've built or optimized, quantifying performance improvements

2

Demonstrate your experience with cost-efficient scaling—mention specific techniques you've used to reduce training costs while maintaining model quality

3

Showcase projects where you implemented dataset/model versioning and lineage tracking systems for reproducible fine-tuning at scale

4

Connect your technical experience to sustainability—explain how your work in efficient AI infrastructure aligns with Crusoe's mission of eco-friendly computing

5

Include concrete metrics about the scale of LLMs you've worked with (parameter counts, training data size, cluster sizes) and the efficiency gains you achieved

✉️ What to Emphasize in Your Cover Letter

['Your experience with end-to-end training pipelines for Large Language Models and specific fine-tuning techniques mentioned (SFT, PEFT, LoRA, adapters)', 'Examples of leading initiatives in AI infrastructure that resulted in measurable improvements in efficiency, reliability, or cost reduction', "How your background in sustainable or efficient computing aligns with Crusoe's mission of transforming stranded energy into eco-friendly AI infrastructure", 'Specific contributions to dataset/model/experiment management systems with versioning, lineage, and reproducible fine-tuning capabilities']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Crusoe's specific stranded energy projects and how they power data centers—understand their unique energy-to-compute model
  • → Their public statements about AI infrastructure and sustainability goals to align your application with their mission
  • → Technical blog posts or talks by Crusoe engineers about their current AI/ML infrastructure challenges and approaches
  • → Their partnerships and customer base to understand the practical applications of the models you'd be working on

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Deep dive into your experience with multi-node orchestration and failure recovery for LLM training—specific tools, challenges, and solutions
2 Technical discussion of implementing PEFT/LoRA techniques and optimizing them for production environments at scale
3 Design questions about building agent execution infrastructure and integrating it with existing training pipelines
4 Case study on optimizing training costs while maintaining model quality, including trade-offs you've made in real projects
5 Questions about your approach to dataset and experiment management for reproducible research at scale across teams
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⚠️ Common Mistakes to Avoid

  • Focusing only on model development without demonstrating deep infrastructure experience (multi-node orchestration, checkpointing, scaling)
  • Presenting generic AI experience without specific examples of working with the mentioned techniques (SFT, PEFT, LoRA, distillation/RL pipelines)
  • Failing to connect your technical background to efficiency/sustainability—Crusoe specifically cares about cost-efficient, eco-friendly computing

📅 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 Crusoe!

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