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# Staff Software Engineer

- **Company:** Crusoe's Inc
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $250,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Files, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Software Engineering, Management of Software Versions, Reinforcement Learning, Pytorch, Transfer Learning, Large Language Models, Generative AI, Information Technology, Golang
- **Published:** November 7, 2026
- **Apply:** https://startup.jobs/staff-software-engineer-ai-model-lifecycle-crusoe-10703705

## About the Role

* Advanced degree in Computer Science, Engineering, or a related field.
 * 4-5+ years of industry experience leading and driving impactful projects in the AI Space
 * Experience in Generative AI (Large Language Models, Multimodal).
 * Hands-on experience training, fine-tuning, and aligning LLMs using Reinforcement Learning and Reinforcement Fine-Tuning (RFT) techniques.
 * Proactive and collaborative approach with the ability to work autonomously
 * Passion for building cutting-edge AI products and solving challenging technical problems.

Bonus Points:

 * Proficiency in Golang or Python for large-scale, production-level services and PyTorch
 * Contributions to open-source AI projects such as vLLM or similar frameworks.
 * Performance optimizations on GPU systems and inference frameworks.

## Description

The Senior Staff Software Engineer for the AI Model Lifecycle team will play a crucial role in building a comprehensive managed platform for the entire application development lifecycle, with a specific focus on leveraging Machine Learning models, including Large Language Models (LLMs).

What You'll Be Working On:

 * Manage fine-tuning systems for large foundation models (SFT, PEFT, LoRA, adapters), including multi-node orchestration, checkpointing, failure recovery, and cost-efficient scaling.
 * Implement and maintain end-to-end training pipelines for Large Language Models.
 * RFT and Reinforcement learning to the fine tuning and training sections
 * Distillation and reinforcement learning pipelines (e.g., preference optimization, policy optimization, reward modeling).
 * Dataset, model, and experiment management: versioning, lineage, evaluation, and reproducible fine-tuning at scale.