InternshipExternal listing

LLM Evaluation Intern

UnknownArtificial Intelligence / Natural Language ProcessingRemotePosted September 17, 2026

About this role

This listing aggregates 21+ latest LLM Evaluation internships available on Internshala, including paid opportunities across multiple hiring organizations. As an LLM Evaluation Intern, you will be responsible for testing, benchmarking, and assessing the quality, safety, and reliability of large language models across a variety of tasks including reasoning, summarization, translation, code generation, and instruction following. You will design and execute evaluation pipelines, build automated scoring rubrics, analyze model outputs, and document findings in clear technical reports. The role involves comparing LLM responses against ground-truth references, identifying hallucinations, biases, and failure modes, and providing actionable feedback to model development and research teams. Interns may also contribute to red-teaming exercises, prompt engineering experiments, and quality assurance workflows for AI products. This is an excellent opportunity for candidates passionate about AI safety, NLP, and responsible AI deployment to gain hands-on experience with frontier language models. The internship is open to students and recent graduates with a strong foundation in machine learning and natural language processing. Specific responsibilities include running evaluation benchmarks, performing manual and semi-automated content review, maintaining evaluation datasets, collaborating with engineering and research teams, and presenting insights to stakeholders. Duration and compensation vary by hiring company, with several positions offering stipends. Interested candidates can explore all available openings and apply directly through the Internshala platform using the link provided.

Requirements

  • See the original listing for full requirements.

Quick Info

Employment Type

Internship

Location

Remote

Department

Artificial Intelligence / Natural Language Processing

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