Senior Software Engineer, AI Model Lifecycle

Crusoe

$172K — $230K *
Enterprise Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science, Engineering, or related field
  • 4-5+ years of industry experience in software engineering
  • Experience in delivering production-ready features
  • Familiarity with cloud-based services (compute, storage, networking)
  • Familiarity with Generative AI, specifically Large Language Models (LLMs)
  • Proficiency in Golang or Python for production services
  • Basic experience with training and fine-tuning LLMs

Responsibilities

  • Implement and maintain fine-tuning systems for large foundation models
  • Develop and maintain end-to-end training pipelines for LLMs
  • Build components for distillation and reinforcement learning pipelines
  • Maintain core agent execution infrastructure
  • Implement features for dataset, model, and experiment management
  • Collaborate with engineers and product teams on system abstractions and APIs
  • Engage with the open-source LLM ecosystem

Benefits

  • Industry competitive pay
  • Restricted Stock Units in a fast-growing technology company
  • Health insurance options, including HDHP and PPO, vision, and dental
  • Employer contributions to HSA accounts
  • Paid Parental Leave
  • Life insurance and disability coverage
  • Teladoc access
  • 401(k) with a 100% match up to 4%
  • Generous paid time off and holidays
  • Cell phone reimbursement
  • Tuition reimbursement
  • Subscription to the Calm app
  • Company paid commuter benefit up to $300/month
Full Job Description
About This Role:

The Senior 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.


What You'll Bring to the Team:
  • 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.


Benefits:
  • Competitive compensation
  • Restricted Stock Units
  • Paid time off & paid holidays
  • Comprehensive health, dental & vision insurance
  • Employer contributions to HSA account
  • Paid parental leave
  • Paid life insurance, short-term and long-term disability
  • Professional development & tuition reimbursement
  • Mental health & wellness support
  • Commuter benefits (parking & transit)
  • Cell phone stipend
  • 401(k) Retirement plan with company match up to 4% of salary
  • Volunteer time off


Compensation Range

Compensation will be paid in the range of up to $172,425 - $230,945 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data.

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