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Full Stack Data Engineer - AI Research - Global Tech £92K

Principle HR
London
2 weeks ago
Applications closed

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Full Stack Data Engineer – AI Research | £92K | Hybrid London (3 Days Onsite)

We’re hiring a Full Stack Data Engineer to join an elite AI research team in London. You’ll be hands-on with end-to-end pipeline development—onboarding, filtering and packaging massive datasets (text, image, video) that will power LLM agents used in cutting-edge automation and FAIR research.

This isn’t your average data role—it’s technical, research-focused, and sits right at the intersection of data engineering and software craftsmanship.

What's in it for you?

  • Annual Salary up to £92,000 doe
  • Length: Through December 2025, with possibility of extension
  • Contract Type: Inside IR35 - PAYE - paid via Principle HR on a weekly basis
  • Location: 3 days onsite, Central London


What you’ll be doing:

  • Build Python-based data pipelines to ingest and filter high-risk datasets
  • Clean, transform, validate and repackage data (text/image/video) at scale
  • Work closely with data engineers and researchers to mitigate risk in datasets
  • Deliver production-grade filtering solutions with minimal overhead
  • Participate in daily research discussions—this is an engineering-first role with a research edge


Ideal profile:

  • Solid Python engineer with proven experience in data pipelines
  • Comfortable across the stack: you can build tools, services, scripts—not just run notebooks
  • Skilled in dataset wrangling, filtering, and data quality control
  • Exposure to machine learning or FAIR/LLM-style projects is a strong advantage
  • Experience with JavaScript or PyTorch is a bonus, but not required
  • Able to thrive in a fast-paced, highly collaborative research team


No fluff. No layers. This is a sharp role with full access to the research team. If you’ve got full stack capability and a passion for data integrity at scale—let’s talk.

Apply Now with your CV or LinkedIn profile today. Som |

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National AI Awards 2025

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