1

Intern Llm Trainer Jobs (NOW HIRING)

Role We are seeking a highly motivated Machine Learning Research Intern to work on cutting-edge ... Conduct original research in the broad areas of LLM training and evaluation, RAG, RL and AI agents ...

We're looking for an intern to help build the data foundation for this agent, with a focus on ... LLM-assisted data processing. * Familiarity with machine learning workflows, model training ...

The intern will also have the opportunity to reshape Samaya's key product roadmap using their ... Conduct original research in the broad areas of LLM training and evaluation, RAG, RL and AI agents ...

Role Overview As an Applied Research intern at Labelbox, you will design, build, and productionize ... Passion and experience for LLM evaluation and benchmarking. * Expertise in training data quality ...

We are seeking a highly motivated PhD intern to work on LLM-based agents for end-to-end software ... Improve agent performance using training data, SFT/RL methods, and test-time verification. * Run ...

AI Intern

San Antonio, TX · On-site

$13.50 - $18/hr

... training workflows, evaluation, packaging, and deployment processes. * Support deployment and ... Experience with LLM application patterns, such as: Retrieval-Augmented Generation (RAG), Prompt ...

next page

Showing results 1-20

Intern Llm Trainer information

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

To thrive as an Intern LLM Trainer, you should have a solid understanding of natural language processing, machine learning fundamentals, and strong programming skills, often supported by coursework or experience in computer science or related fields. Familiarity with tools like Python, PyTorch or TensorFlow, and version control systems such as Git is typically required, along with exposure to data labeling platforms. Attention to detail, strong analytical thinking, and effective communication are vital soft skills for collaborating on model improvement and troubleshooting. These competencies enable interns to contribute meaningfully to the development and refinement of language models, ensuring high-quality outcomes and impactful learning experiences.

What are some typical responsibilities and learning opportunities for an Intern LLM Trainer during their internship?

As an Intern LLM Trainer, you will typically assist in preparing and curating training datasets, annotating data, and running model evaluations under the guidance of senior machine learning engineers. You'll also gain hands-on experience in fine-tuning large language models (LLMs) and analyzing their performance. This role offers the opportunity to learn best practices in prompt engineering, collaborate with data scientists and software engineers, and gain exposure to the latest advancements in natural language processing. Regular feedback and mentorship are common, helping you develop both technical and teamwork skills.

What does an Intern LLM Trainer do?

An Intern LLM Trainer assists in training large language models (LLMs) by curating datasets, annotating data, and evaluating model outputs. They work closely with machine learning engineers and data scientists to improve the accuracy and performance of AI models. This role often involves researching new methods, running experiments, and providing feedback to enhance natural language understanding. Interns may also help document processes and support model deployment in real-world applications.

What is the difference between Intern Llm Trainer vs Data Annotator?

AspectIntern Llm TrainerData Annotator
Required CredentialsBasic understanding of machine learning, NLP, or AI; often pursuing related degreesHigh school diploma or equivalent; no specialized credentials typically needed
Work EnvironmentTech companies, AI startups, research labs; collaborative and project-basedData labeling firms, tech companies; focused on data preparation tasks
Employer & Industry UsageAI development, machine learning teams, research projectsData management, AI training datasets, quality assurance

Intern Llm Trainers focus on training language models through supervised learning and require some technical knowledge, while Data Annotators primarily label data to prepare datasets. Both roles are essential in AI development but differ in technical complexity and responsibilities.

More about Intern Llm Trainer jobs
What cities are hiring for Intern Llm Trainer jobs? Cities with the most Intern Llm Trainer job openings:
What are the most commonly searched types of Llm Trainer jobs? The most popular types of Llm Trainer jobs are:
What states have the most Intern Llm Trainer jobs? States with the most job openings for Intern Llm Trainer jobs include:

ML Research Intern

Samaya AI

Mountain View, CA

Other

Re-posted 17 days ago


Job description

Role

We are seeking a highly motivated Machine Learning Research Intern to work on cutting-edge research in the fields of Natural Language Processing (NLP) and Machine Learning (ML). At Samaya, our product delivers value to one of the most high-stake environments of AI applications - helping financial professionals make knowledge-informed decisions via key techniques such as RAG, RL and AI agents with advanced tool calling and long-horizon reasoning, etc. The primary focus of this internship is to conduct novel research that advances these fields, with the goal of publishing in top-tier ML and NLP conferences. The intern will collaborate closely with our team of experienced researchers on topics aligned with the company's mission.

This role is ideal for a PhD or advanced Master's student passionate about fundamental research and real-world AI applications. The intern will also have the opportunity to reshape Samaya's key product roadmap using their research.

Responsibilities
  • Conduct original research in the broad areas of LLM training and evaluation, RAG, RL and AI agents, leading to publications in top ML/NLP conferences
  • Develop and experiment with new models, algorithms, and methodologies to improve AI agents performance in real-world tasks
  • Collaborate with in-house researchers to design, implement, and evaluate novel methods in alignment with the company's mission
  • Present findings internally and externally through research papers and technical talks
Qualifications
  • Currently pursuing a PhD or Master's degree in Computer Science, Machine Learning, NLP, or a related field
  • Strong background in deep learning, large language models, and NLP techniques. Deep domain knowledge with RAG, RL and AI agent training and evaluation is a plus
  • A strong track record of first-author publications in top AI/NLP conferences (e.g., NeurIPS, ICML, ACL, EMNLP)
  • Proficiency in Python and deep learning frameworks such as PyTorch or Transformers, and strong coding skills