Senior Applied AI Scientist (AI Marketplace)
PreplyLondonPosted 27 March 2026
Job Description
Senior Applied AI Scientist (AI Marketplace)
WE POWER PEOPLE’S PROGRESS.
At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalised learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled - and it’s creating real impact.
We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human-led, AI-enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries - and we’re only getting started. As a category-defining company, we’re shaping what the future of learning looks like at global scale.
Every Preply lesson sparks change, fuels ambition, and drives progress that matters. Joining Preply means helping define the future of education at global scale, and building something that truly matters for millions of people, every day.
MEET THE TEAM!
At Preply, AI isn't a feature — it's foundational. It powers how we connect millions of learners and tutors, personalise every interaction, and build a marketplace where meaningful learning thrives at global scale.
The Applied AI team sits at the centre of this ecosystem. We design and deploy intelligent systems that drive real-time tutor–learner matchmaking across millions of sessions worldwide. Over time, we've evolved from traditional machine learning approaches into a modern AI-native marketplace — leveraging deep learning, advanced ranking and re-ranking architectures, and proprietary multimodal data including audio, video, and transcripts to deliver smarter recommendations and measurable learning outcomes.
Our scope extends far beyond recommendations. We build and operate the intelligence layer that ensures the marketplace is not only high-performing, but also safe and trustworthy. From neural ranking models in PyTorch to large-scale experimentation infrastructure and full-spectrum Trust & Safety systems, including fraud prevention, abuse detection, and marketplace integrity, we tackle some of the most complex and high-impact challenges in the business.
As part of Applied AI, you'll partner closely with product, engineering, and data leadership to bring cutting-edge research into production at scale. This is where state-of-the-art modelling meets real-world constraints, and where every incremental model improvement translates into better matches, stronger trust, and real progress for learners and tutors around the world.
WHAT YOU’LL BE DOING:
- AI Research & Development: Apply state-of-the-art, multimodal neural search & Ranking models to build and optimize scalable machine learning solutions for personalized learning and AI tutors.
- Applied Science Execution: Work on cutting-edge AI models, contributing to research, prototyping, and deployment in production environments.
- Cross-functional Collaboration: Partner with scientists, engineers, and product teams to deliver AI-driven solutions that improve learning experiences and business outcomes.
- Mentorship & Knowledge Sharing: Support and mentor junior scientists and engineers, fostering an environment of learning and innovation.
- Best Practices in ML: Contribute to machine learning best practices, including model architecture, research methodologies, and deployment pipelines.
WHAT YOU NEED TO SUCCEED:
- PhD in Information Retrieval (IR), NLP, Machine Learning, or a related field (or equivalent experience)
- 4+ years of industry experience, focusing on deep learning-based NLP and IR applications.
- Strong proficiency in deep learning frameworks such as PyTorch or TensorFlow.
- Hands-on experience with cloud platforms (e.g., AWS, Google Cloud).
- Ability to drive AI initiatives from research to production in a collaborative environment.
- Strong problem-solving skills and a track record of building impactful AI solutions.
- Passion for advancing AI-driven personalized learning experiences.
- Eagerness to wor ... (truncated, view full listing at source)
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