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Remote Library Foundation Jobs (NOW HIRING)

University of Wisconsin-Madison

Accountant

Madison, WI · On-site +1

$55K - $65K/yr

Work with Wisconsin Foundation and Alumni Associate (WFAA) to oversee incoming gift deposits ... A remote work agreement may allow for a maximum of 20% remote per week. This position is full-time, ...

Astra

Senior Fullstack Engineer

Strong React and TypeScript skills are the foundation. Python fluency is a plus, not a prerequisite ... Establish and maintain frontend best practices, component libraries, and design patterns for a ...

IP House

Proposal Development Coordinator

$40K - $50K/yr

Help maintain a library of proposal content such as templates, case studies, and team bios. * Keep ... We believe our people are the foundation of everything we do-and we invest accordingly. From day ...

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How much do remote library foundation jobs pay per year?

As of Jun 10, 2026, the average yearly pay for remote library foundation in the United States is $62,056.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $72,500.00 per year, depending on experience, location, and employer.
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GenAI Engineer - Remote

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Posted 22 days ago

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs—particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

  • Enables domain-specific fine-tuning of models to client's unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:

  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to client's domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:

  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content