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CrusoeCrusoeSan Francisco, CA

Senior Staff Software Engineer, AI Model LifeCycle

Builds and manages platforms for AI model lifecycles, focusing on fine-tuning, training pipelines, and reinforcement learning for LLMs. Requires 8+ years in AI, advanced degree, and hands-on experience with generative AI techniques.

238k – 318k/yr
On-site8+ YOEML Engineering

About the role

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.

What You’ll Bring to the Team

  • Advanced degree in Computer Science, Engineering, or a related field.
  • 8+ 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.

Compensation

Compensation range: $237,600 - $318,240 + Bonus. Restricted Stock Units included.

Skills

PyTorchLLMsReinforcement LearningFine-TuningLoraPeftSftRftvLLMGoPythonGPUMulti-Node OrchestrationDataset VersioningModel Evaluation

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