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Llm Fine Tune Model Jobs (NOW HIRING)

LLM Algorithm Engineer

Menlo Park, CA · On-site

$150 - $200/hr

The role We are looking for a Large Language Model Algorithm Engineer to help build, fine-tune ... Experience with LLM fine-tuning or post-training, such as SFT, DPO, or RLHF, is preferred.

$125 - $150/hr

Monitor, evaluate, and fine-tune model accuracy and reliability in production. Requirements * 3+ ... Experience in building and integrating LLM applications, LangChain, or agentic workflows. * Solid ...

AI Engineer

Menlo Park, CA · On-site

$125 - $150/hr

Fine-tune proprietary and open-source large language models for travel-specific applications ... Experience with LLM fine-tuning, RLHF, and prompt engineering * Knowledge of model optimization ...

Key Responsibilities: * Independently build, train, and fine-tune AI/ML models. * Apply data ... Experience with NLP, LLM, or GenAI tools (e.g. LoRA, LangChain, RAG, LLM Fine Tuning). * Hands-on ...

Fine-tune foundation models on proprietary data and implement novel techniques to achieve world ... and deploying LLM-based systems (fine-tuning, RAG, or agentic workflows) in a production ...

Fine-tune models for performance and efficiency. * Troubleshooting: Address and resolve issues related to generative AI models and implementations. * Documentation: Create and maintain comprehensive ...

GEN AI Architect - New Jersey

Somerville, NJ · On-site

$65.25 - $84/hr

Fine-tune models for performance and efficiency. * Troubleshooting: Address and resolve issues related to generative AI models and implementations. * Documentation: Create and maintain comprehensive ...

Senior LLM Engineer

Austin, TX · On-site

$195K - $255K/yr

Fine-tune foundation models (LoRA/QLoRA, RLHF/DPO, instruction tuning) for domain-specific tasks and terminology. * Build agentic and tool-use workflows that connect the LLM to internal engineering ...

Senior AI/LLM Engineer

$179K - $201K/yr

Develop and fine-tune LLMs and RAG pipelines to reason over Internet-scale data, summarize findings ... Continuously evaluate and improve model performance through RAGAS, LangSmith, and automated ...

Senior AI/LLM Engineer

$179K - $201K/yr

Develop and fine-tune LLMs and RAG pipelines to reason over Internet-scale data, summarize findings ... Continuously evaluate and improve model performance through RAGAS, LangSmith, and automated ...

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Llm Fine Tune Model information

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$140.5K

$166.2K

$193.5K

How much do llm fine tune model jobs pay per year?

As of Sep 8, 2026, the average yearly pay for llm fine tune model in the United States is $166,249.00, according to ZipRecruiter salary data. Most workers in this role earn between $158,000.00 and $170,000.00 per year, depending on experience, location, and employer.

What is an LLM fine-tune model?

An LLM fine-tune model refers to a large language model (LLM) that has been further trained on a specific dataset to specialize in certain tasks or domains. Fine-tuning allows organizations or individuals to adapt a general-purpose LLM, such as GPT or BERT, to better understand and respond to domain-specific language, requirements, or user needs. This process improves the model's accuracy, relevance, and usefulness for specialized applications. Fine-tuning typically involves using transfer learning techniques and requires a curated dataset for the desired task.

What are the key skills and qualifications needed to thrive as an LLM fine-tune model engineer?

To thrive as an LLM Fine-Tune Model Engineer, you need a strong background in machine learning, natural language processing, and programming (typically Python), often supported by a degree in computer science or related fields. Experience with deep learning frameworks (such as PyTorch or TensorFlow), model evaluation tools, and familiarity with cloud platforms or MLOps tools is essential. Analytical thinking, attention to detail, and effective communication help in troubleshooting, interpreting results, and collaborating with cross-functional teams. These skills ensure the development of robust, accurate, and scalable language models tailored to specific business needs.

What are some common challenges faced when fine-tuning large language models (LLMs) in a professional setting?

Fine-tuning large language models often involves handling vast datasets, ensuring data privacy, and balancing computational resource constraints. Professionals in this role must troubleshoot issues related to overfitting, bias in training data, and model drift. Collaboration with data engineers, domain experts, and MLOps teams is crucial to ensure the model meets specific business needs while maintaining ethical and performance standards.

What is the difference between Llm Fine Tune Model vs Data Scientist?

AspectLlm Fine Tune ModelData Scientist
Required CredentialsKnowledge of machine learning, NLP, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development teams, research labsBusiness, research, analytics teams
Industry UsageAI, tech companies, startupsFinance, healthcare, marketing, tech

The main difference is that an Llm Fine Tune Model focuses on customizing large language models for specific tasks, while a Data Scientist analyzes data to generate insights and build models. Both roles require programming and analytical skills, but their applications and focus areas differ significantly.

Infographic showing various Llm Fine Tune Model job openings in the United States as of September 2026, with employment types broken down into 5% Internship, 50% Full Time, and 45% Contract. Highlights an 80% In-person, 5% Hybrid, and 15% Remote job distribution, with an average salary of $166,249 per year, or $79.9 per hour.

LLM Algorithm Engineer

Menlo Park, CA • On-site

$150 - $200/hr

Other

Posted 7 days ago


Job description

Menlo Park, CA · 5 days/week in office · Full-time

About Corepass

We operate at the intersection of frontier model research and production-grade applied AI. Our customers are not looking for demos. They need agents that drive revenue, support underwriting decisions, and perform reliably in real business environments.

We are early, well-resourced, and moving fast.

The role

We are looking for a Large Language Model Algorithm Engineer to help build, fine-tune, align, optimize, and deploy large language models for real-world AI systems. This role focuses on model training, post-training, domain-specific model development, evaluation, inference optimization, and integration into agent systems.

Responsibilities
  • Own fine-tuning, post-training, and performance optimization for large language models.
  • Build domain-specific LLMs, including data design, training, and evaluation.
  • Lead model alignment efforts, including SFT, DPO, RLHF, and related data strategy optimization.
  • Explore reinforcement learning applications in large language models, including PPO, actor-critic methods, and related approaches.
  • Build an end-to-end model iteration loop, covering data, training, evaluation, and continuous improvement.
  • Contribute to model inference optimization, including distillation, quantization, acceleration, and deployment.
  • Support the practical deployment of models in agent-based systems.
Requirements
  • Master’s degree or above in computer science, mathematics, artificial intelligence, or a related field.
  • Strong foundation in deep learning and reinforcement learning.
  • Solid understanding of Transformer architecture and large-scale model training workflows.
  • Experience with LLM fine-tuning or post-training, such as SFT, DPO, or RLHF, is preferred.
  • Proficient in PyTorch and familiar with Linux and GPU-based development environments.
  • Strong modeling ability and engineering implementation skills.
  • Strong sense of ownership and a continuous improvement mindset.
Why Corepass
  • Foundational work: Join an early team building both model infrastructure and applied AI products.
  • Real product focus: We train our own models and build agents designed for production use, not demos.
  • High-impact problems: Our agents are built for business-critical workflows such as lead generation and underwriting.
  • Fast execution: We are a tight team with low bureaucracy and a strong bias toward shipping.
  • In-person culture: We work from our SF Bay Area office five days a week.

Corepass is an equal opportunity employer. We hire on merit and welcome candidates of every background.

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