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Intern Llm Developer Jobs in Santa Rosa, CA (NOW HIRING)

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What are the key skills and qualifications needed to thrive as an Intern LLM Developer, and why are they important?

To thrive as an Intern LLM Developer, you generally need a solid background in computer science, programming (especially Python), and foundational knowledge of machine learning concepts. Familiarity with deep learning frameworks like TensorFlow or PyTorch, version control systems such as Git, and exposure to large language models (LLMs) are typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and learn in dynamic team environments. These skills and qualities are vital for contributing to cutting-edge AI projects and rapidly adapting to evolving technologies in the field.

What types of projects and learning opportunities can an Intern LLM Developer expect to work on during their internship?

As an Intern LLM Developer, you can expect to participate in hands-on projects involving the development, fine-tuning, or evaluation of large language models (LLMs). Typical responsibilities include data preprocessing, implementing model training pipelines, and collaborating with senior engineers and data scientists to optimize model performance. You'll also have opportunities to contribute to research on natural language processing (NLP) tasks and gain exposure to industry-standard tools and frameworks. This role offers valuable mentorship and the chance to build practical skills in machine learning and AI, setting a strong foundation for a future career in the field.

What does an Intern LLM Developer do?

An Intern LLM (Large Language Model) Developer supports the development, testing, and deployment of AI models, specifically large language models like GPT or BERT. Their responsibilities often include data preprocessing, model fine-tuning, writing code to interact with APIs, and evaluating model performance. Interns work under the guidance of senior developers and researchers to gain hands-on experience in natural language processing and AI. This role is ideal for students or recent graduates looking to build practical skills in machine learning and AI development.

What is the difference between Intern Llm Developer vs Intern Machine Learning Engineer?

AspectIntern Llm DeveloperIntern Machine Learning Engineer
Required CredentialsTypically pursuing or recent graduate in Computer Science, AI, or related fieldsSimilar educational background, often with focus on ML or AI
Work EnvironmentTech companies, AI startups, research labsTech firms, startups, research institutions
Employer & Industry UsageFocused on developing large language models and NLP applicationsDeveloping various ML models, including NLP, computer vision, etc.
Common Search & ComparisonIntern Llm Developer vs Intern Machine Learning Engineer

Intern Llm Developers primarily focus on building and fine-tuning large language models, often specializing in NLP tasks. Intern Machine Learning Engineers have a broader scope, working on various ML models across different domains. Both roles require similar educational backgrounds and are found in tech and AI industries, but their specific focus areas differ.

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What job categories do people searching Intern Llm Developer jobs in Santa Rosa, CA look for? The top searched job categories for Intern Llm Developer jobs in Santa Rosa, CA are:
What cities near Santa Rosa, CA are hiring for Intern Llm Developer jobs? Cities near Santa Rosa, CA with the most Intern Llm Developer job openings:
Intern Llm Developer jobs near you
Research Intern - Reinforcement Learning (RL) - Onsite

Research Intern - Reinforcement Learning (RL) - Onsite

Level AI

Bodega Bay, CA

$17.75 - $23.75/hr

Other

Posted yesterday

Job description

 Build the next generation of Agentic AI with us

Our platform combines conversation intelligence, multimodal understanding, and agentic AI systems to power both human agents and autonomous AI agents across the entire customer experience lifecycle.

A core part of this vision is our investment in custom Small Language Models (SLMs)-purpose-built for CX workflows-paired with reinforcement learning systems that continuously improve decision-making in real-world environments.

We're looking for a Research Intern (Reinforcement Learning) to join us in shaping this future.

What you'll do
 

  • Design and build reinforcement learning environments that model real-world customer interaction workflows.

  • Design RL agents that learn from these environments using real-world interaction data, rewards, and feedback loops

  • Define reward models and feedback loops using real-world signals (outcomes and human feedback)

  • Enable learning from production data by structuring interaction traces into training-ready datasets for offline and online learning

  • Experiment with multi-agent systems and simulation frameworks for complex coordination and decision-making

  • Collaborate with engineering and product teams to deploy, evaluate, and iterate on learning systems in production at scale.

What we're looking for

  • Currently pursuing (or recently completed) a degree in Computer Science, AI, Machine Learning, or related field

  • Strong understanding of reinforcement learning fundamentals

  • Familiarity with RL environments and training libraries such as Verl and Tinker

  • Strong foundation in probability, math, and optimization

  • Passion for building real-world AI systems

Nice to have

  • Experience with RLHF, LLM/SLM fine-tuning, or model alignment

  • Exposure to agent-based systems or multi-agent RL

  • Prior research, projects, or publications in RL or applied ML

  • Experience working with large-scale or production datasets

Why Level AI

  • Work on production-grade Agentic AI systems used by leading enterprises

  • Build alongside a team with deep expertise from Amazon, Google, and Meta

  • Be part of a fast-growing Series C AI company.

  • Direct exposure to 01 AI innovation in CX and decisioning systems

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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