About DAM

DAM is building evidence-backed AI systems designed to assess claims, identify weak or conflicting evidence, and make information more reliable. We are assembling a focused LLM data team to help build the data and evaluation foundations behind this work.

Role: LLM Data & Evidence Intern

Duration: 60 days

Location: Remote

Type: Unpaid internship

Openings: Up to 10 interns

What you’ll work on

You will contribute to the data and pipeline work that supports DAM’s LLM systems. The work is practical, detail-oriented, and directly connected to how evidence-backed AI is built.

Your responsibilities may include

- Collecting targeted public datasets from the web for specific research or model-development requirements
- Web scraping, data extraction, cleaning, structuring, and deduplication
- Finding, assessing, and recording reliable sources for claims and factual information
- Normalising evidence into consistent formats, including source metadata, dates, excerpts, URLs, and provenance
- Supporting claim categorisation, quality checks, and dataset preparation
- Assisting with internal data pipelines, documentation, and evaluation workflows
- Writing small Python utilities or scripts where needed

Minimum requirements

- Basic working knowledge of Python
- Foundational understanding of LLMs and how training/evaluation data affects model quality
- Ability to collect structured data from the internet through web scraping, APIs, or public datasets




- Careful research habits and solid attention to source quality, accuracy, and documentation
- Ability to work independently, follow a defined process, and communicate progress clearly

Preferred, but not required
- Familiarity with requests, Beautiful Soup, Selenium, Scrapy, or similar web-data tools
- Experience handling JSON, CSV, APIs, Git/GitHub, SQL, or data-cleaning workflows
- Interest in AI safety, misinformation, fact verification, data quality, or applied machine learning
- Comfort writing production-oriented code or contributing to small technical systems

What you’ll receive

- Direct, hands-on experience working on real LLM data, evidence, and pipeline problems
- Exposure to the workflow behind building evidence-backed AI products
- A Letter of Completion upon successful completion of the 60-day internship
- An individual Letter of Recommendation for interns whose work, reliability, and contribution merit one
- High-performing interns may be considered for future roles or continued collaboration with DAM. This is not a guaranteed conversion.

Important note

This is an unpaid, learning-focused internship. Please apply only if you are comfortable with the 60-day unpaid arrangement and are genuinely interested in contributing to LLM data and evidence workflows.

How to apply

Apply through LinkedIn with your CV. If available, please also include your GitHub, portfolio, or one example of a Python, scraping, data, or AI-related project.

📌 LLM Data & Evidence Intern (India)
🏢 DAM
📍 India