Job Title
Senior Machine Learning Scientist
Salary
Not Disclosed
Company Description
Chattermill Analytics Limited is a Series B funded, AI-native customer experience intelligence platform based in London. They build proprietary NLP models that help global brands process and understand messy, real-world customer feedback at scale, moving beyond simple API wrapping to develop truly custom machine learning architectures and agentic systems.
Job Description
Join a small, senior team to build and ship the next generation of NLP models. You will own the end-to-end pipeline, from training and fine-tuning custom transformer models to deploying production-ready agentic systems. This role is perfect for a scientist who wants to solve complex text-analysis problems using bespoke, high-performance solutions.
Location
Remote, Europe
Why this role is remarkable
- Work on a proprietary ML stack involving extraction, retrieval, and summarization rather than just wrapping third-party APIs or basic LLM prompts.
- Join a high-performing Series B company where you have direct ownership over significant parts of the model pipeline and immediate product impact.
- Collaborate in an elite, senior environment with direct mentorship opportunities while exploring cutting-edge agentic and RAG architectures for customer intelligence.
What You Will Do
- Train, evaluate, and iterate on custom LLMs and transformer architectures using PyTorch, custom training loops, and parameter-efficient fine-tuning like LoRA.
- Build and maintain advanced retrieval pipelines, reranking systems, and data mining agents to extract insights from large-scale, messy customer feedback.
- Design robust evaluation frameworks and metrics to benchmark non-deterministic systems and automate model comparisons before shipping production-quality code to customers.
The ideal candidate
- Holds an MSc or PhD in Machine Learning, Computer Science, or a related STEM field with deep expertise in modern transformer architectures.
- Possesses strong PyTorch skills, including the ability to write custom training loops and perform tensor-level debugging for production-grade NLP models.
- Has practical experience building and serving large-scale generative AI or agentic systems using frameworks such as vLLM, DeepSpeed, or PEFT techniques.
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