Senior Machine Learning Scientist

🦸‍♀️ The Role 🦸‍♀️

Our mission is to help large successful brands like Uber, Amazon, Wise, HelloFresh (and more!) put their customers at the centre of everything they do. Using best-in-class tech in a fast-developing AI space, our Customer Experience Intelligence platform continuously analyses explicit and implicit feedback to enable our clients to identify what they should do next.

We're hiring a Senior Machine Learning Scientist to join the team and help build and ship the next generation of that stack.

👉 What you'll be doing:

Unlike many companies, we use our own custom models, specialised for customer feedback, across various parts of the stack: extraction, retrieval, reranking, summarisation, and sentiment analysis. We are also pragmatic and understand that the right solution can be a combination of off-the-shelf LLMs, bespoke fine-tuned models, and sometimes techniques that utilise no LLM at all.

This means you will:

• Train, evaluate, and iterate on ML models and agentic systems for customer feedback, including owning our custom fine-tuning pipelines. Run experiments end-to-end, track results rigorously, and make clear recommendations on what to ship, iterate, or retire.
• Build and maintain LLM-powered features: retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation.
• Design and run robust evaluation frameworks: build test sets, define metrics, evaluate non-deterministic systems, handle class imbalance, and automate checkpoint comparisons.
• Improve and extend semantic search and retrieval, evolving from embedding-based approaches toward more advanced methods.
• Write production-quality code and collaborate closely with Engineering on productionisation, model serving, data pipelines, and monitoring.
• Work with Product and Commercial teams to translate business needs into practical ML solutions, and support client evaluations and accuracy benchmarking.
• Mentor team members, review code and research, and bring relevant advances from the literature into the product.

🧰 What you’ll need:

• A deep working knowledge of transformer architectures.
• Strong PyTorch skills, with the ability to write custom training loops, modify model architectures, and debug issues at the tensor level. Ideally, experience with parameter-efficient fine-tuning techniques such as LoRA.
• Extensive experience working with large-scale, messy real-world text data, including classification, extraction, embeddings, re-rankers, clustering, and search.
• Experience in instruction fine-tuning and serving language models, familiarity with frameworks such as vLLM, DeepSpeed, or similar tools.
• A solid grounding in classical ML and statistics, and the judgement to choose simpler methods when they’re the right solution.
• Practical experience building with GenAI and agentic patterns.
• Excellent communication skills and confidence translating complex technical concepts for non-technical audiences (and vice versa!).
• Technical curiosity and a keen interest in AI – a love of experimenting to make the most of available technology.
• High ownership and initiative, with the ability to identify problems, prioritise effectively, and drive solutions forward.

➕It would be a bonus if you:

• Have an MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Computational Linguistics, or a closely related STEM field.
• Experience with reinforcement learning techniques, such as with verifiable reward (RLVR).