At a Glance
- Tasks: Build cutting-edge AI systems for content and audio experiences.
- Company: Globally recognised media and information business with a start-up vibe.
- Benefits: Competitive salary, hybrid work model, and innovative projects.
- Other info: High-autonomy environment with opportunities for rapid prototyping.
- Why this job: Join a dynamic team shaping the future of AI-native experiences.
- Qualifications: Experience in LLM systems and a passion for multimodal AI.
The predicted salary is between 120000 - 120000 € per year.
Do you want to help build the next generation of AI-native content and audio experiences? Have you built LLM systems where output quality, tone, and trust genuinely mattered? Are you ready to work on multimodal AI products spanning text, voice, and conversational interfaces?
A globally recognised media and information business is building a brand-new internal AI Lab focused on next-generation AI-powered consumer experiences. This is a small, high-autonomy innovation team operating like a start-up inside an established international organisation. The team is exploring how conversational AI, retrieval systems, audio experiences, and multimodal generation can reshape how users interact with content and information products.
You’ll work closely with engineering, product, and design stakeholders on genuinely greenfield AI systems. This Senior AI Engineer role is focused on building production-grade LLM systems across fine-tuning, retrieval, summarisation, evaluation, and audio-first AI experiences. It’s a hands-on role with strong ownership and the opportunity to influence the technical direction of a newly formed AI Lab.
Key Responsibilities- Build and deploy production-grade LLM systems
- Design and optimise RAG and retrieval workflows
- Fine-tune models for tone, style, and output quality
- Develop audio-first AI products using TTS/STT workflows
- Build multimodal AI experiences across text, voice, and search
- Create evaluation frameworks for hallucination and response quality
- Prototype and iterate rapidly in a greenfield environment
- Salary: Up to ~£120k base
- Working model: Hybrid (3+ days/week in London)
- Stack: Python, HuggingFace, LangChain, RAG, multimodal AI, TTS/STT, evaluation frameworks
- Sponsorship: To be confirmed
Interested? Please apply below.
Applied Scientist - Content LLMs & Audio AI in London employer: Harnham
Join a pioneering AI Lab within a globally recognised media and information business, where you'll have the autonomy to innovate and shape the future of AI-powered content and audio experiences. With a strong emphasis on employee growth, collaboration, and a start-up culture, this role offers competitive salary packages and the chance to work on cutting-edge multimodal AI projects in a vibrant London setting.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Scientist - Content LLMs & Audio AI in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those working with LLMs and audio AI. A friendly chat can lead to insider info about job openings or even a referral.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with LLM systems and audio experiences. This could be a game-changer during interviews, proving you’ve got what it takes.
✨Tip Number 3
Prepare for technical interviews by brushing up on relevant concepts. Be ready to discuss your experience with Python, HuggingFace, and multimodal AI. We want to see your passion and expertise shine!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Applied Scientist - Content LLMs & Audio AI in London
Some tips for your application 🫡
Show Your Passion for AI:When writing your application, let your enthusiasm for AI and content creation shine through. We want to see how your experiences align with our mission to build innovative AI-native products. Share specific examples of your work with LLM systems and how you’ve tackled challenges in this space.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for the Applied Scientist role. Highlight relevant skills like fine-tuning models and developing audio-first AI products. We love seeing candidates who take the time to connect their background with what we’re looking for!
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured documents that make it easy for us to see your qualifications. Use bullet points where appropriate and avoid jargon unless it’s relevant to the role. Remember, clarity is key!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at StudySmarter. We can’t wait to hear from you!
How to prepare for a job interview at Harnham
✨Know Your LLMs Inside Out
Make sure you brush up on your knowledge of large language models (LLMs) and their applications. Be ready to discuss your experience with fine-tuning models, optimising output quality, and ensuring the right tone and style. This will show that you’re not just familiar with the tech but can also apply it effectively.
✨Showcase Your Multimodal Experience
Since the role involves working on multimodal AI products, prepare examples of any projects where you've integrated text, voice, or audio elements. Highlight how you approached challenges in these areas and what impact your contributions had on the final product.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions about retrieval systems, evaluation frameworks, and TTS/STT workflows. Brush up on relevant Python libraries like HuggingFace and LangChain, and be ready to explain your thought process when designing and optimising these systems.
✨Demonstrate Your Innovation Mindset
This is a greenfield role, so they’ll be looking for candidates who can think outside the box. Prepare to discuss how you’ve prototyped and iterated on ideas in previous roles. Share specific examples of how your innovative thinking led to successful outcomes in AI projects.
