Senior AI Engineer, AI Lab

Who we are Since 1843, The Economist Group has championed independence, excellence and openness, helping people understand and tackle the critical challenges shaping the world. Today, we are building on that legacy as a global media and information-services company powered by digital innovation, analytical rigour and evidence-based insight.

Across our three businesses
-The Economist, Economist Enterprise and Economist Education - we deliver trusted analysis and insights to individuals and organisations in more than 170 countries. United by a shared purpose to drive progress, we empower decision-makers to make sense of change and chart a course through an increasingly complex world.

As a colleague, you will be part of a culture that values ideas, encourages ownership and holds itself to high standards. We invest in people who are curious, thoughtful and adaptable, whether they are launching new products, reporting on global events or harnessing emerging technologies such as AI to improve how we work. Here, fresh thinking is taken seriously, ambition is matched by integrity, and great work is recognised.

Working across disciplines, geographies and perspectives, we are united by a commitment to innovation, excellence and creating meaningful impact.

The Economist Group is a global media and information services company committed to championing progress. We equip individuals and organisations with expertise, insights, and perspectives to navigate change and drive growth.

This is a full-time role at the centre of our new AI Lab, a small team exploring how generative AI might shape the future of Economist journalism. This role will focus on building and fine-tuning LLM-powered systems with a particular focus on editorial tone, style transfer, retrieval workflows, and multimodal generation (especially audio).You’ll ship products from zero-to-one and see your ideas directly influence how millions of readers interact with our journalism.

If you enjoy working close to design, iterating fast, and building novel interactions across text, voice, and visuals, we’d love to hear from you. You'll be one of the first three engineers in a dedicated lab, working alongside the Tech Lead, Design Lead and Product Lead. What You’ll DoFine-tune large language models (LLMs) for style and tone alignment with The Economist’s editorial voice

Design, curate, and manage datasets used for fine-tuning, including versioning and annotation workflows

Build and evaluate RAG pipelines that incorporate retrieval from structured content

Prototype and test TTS (text-to-speech) pipelines for use in audio-first products (leveraging tools like Eleven Labs, OpenAI TTS etc)
Collaborate with infra, frontend, and design leads to ship internal tools and demos that explore GenAI capabilities

Own quality evaluation pipelines (BLEU, ROUGE, editorial scoring, custom evals), including human-in-the-loop feedback loops

Support product experiments where real-time generation, summarisation, or personalization are being tested

Partner directly with journalists and editors to develop novel evaluation metrics that capture the nuances of The Economist's tone and style

Specific Skills and Expertise3+ years experience building with LLMs or NLP pipelines (ideally hands-on with OpenAI, Claude, Cohere, Gemini, Mistral, Hugging Face)

Experience with supervised fine-tuning (SFT), prompt tuning, or instruction tuning on proprietary datasets

Strong understanding of fine-tuning paradigms, from SFT to the principles behind RLHF/RLAIF preference modeling

Strong Python skills, including working with Lang Chain, Hugging Face Transformers, and data pipelines (Pandas, DVC, Weights & Biases)
Comfortable defining and tracking generation quality with eval metrics like BLEU, ROUGE, and building editorial-specific evaluators

Exposure to STT / TTS tools for prototyping (e.g., Whisper, Eleven Labs, Bark, etc.)Strong communication and collaboration mindset — able to work closely with editorial and product stakeholders

Curious and exploratory — comfortable working in ambiguity and pushing the boundaries of GenAI capabilities

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