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Chattermill is building a Customer Experience Intelligence platform that helps major brands (Uber, Amazon, Wise, HelloFresh) analyze customer feedback at scale using advanced AI. The company uses a mix of custom fine-tuned models and off-the-shelf LLMs for extraction, retrieval, reranking, summarization, and sentiment analysis.
As a Senior Machine Learning Scientist, you will own the full lifecycle of ML model development and deployment for customer feedback applications. Your responsibilities include:
- Training, evaluating, and iterating on ML models and agentic systems, with ownership of custom fine-tuning pipelines. You'll run end-to-end experiments, track results rigorously, and make clear recommendations on what to ship, iterate, or retire.
- Building and maintaining LLM-powered features including retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation.
- Designing and running robust evaluation frameworks: building test sets, defining metrics, evaluating non-deterministic systems, handling class imbalance, and automating checkpoint comparisons.
- Improving and extending semantic search and retrieval, evolving from embedding-based approaches toward more advanced methods.
- Writing production-quality code and collaborating closely with Engineering on model serving, data pipelines, and monitoring.
- Working with Product and Commercial teams to translate business needs into practical ML solutions, and supporting client evaluations and accuracy benchmarking.
- Mentoring team members, reviewing code and research, and bringing relevant advances from the literature into the product.
You'll work in a choice-first environment with flexible working arrangements across UK or Poland locations, with the option to work remotely or hybrid as you prefer. The role offers equity participation, professional development budgets, and comprehensive benefits including enhanced family leave (UK), healthcare options, and a dog-friendly London office with rooftop terrace.
REQUIREMENTS:
- Deep working knowledge of transformer architectures
- Strong PyTorch skills: ability to write custom training loops, modify model architectures, and debug at the tensor level; experience with parameter-efficient fine-tuning techniques such as LoRA preferred
- Extensive experience 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
- Solid grounding in classical ML and statistics with judgment to choose simpler methods when appropriate
- Practical experience building with GenAI and agentic patterns
- Excellent communication skills and confidence translating complex technical concepts for both technical and non-technical audiences
- Technical curiosity and keen interest in AI with a love of experimenting
- High ownership and initiative with ability to identify problems, prioritize effectively, and drive solutions forward
BONUS:
- MSc/PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Computational Linguistics, or closely related STEM field
- Experience with reinforcement learning techniques such as verifiable reward (RLVR)
