Senior ML Scientist (NLP/GenAI) - Remote + Equity

Harnham; Greater London; Hybrid; GBP 90,000 - 95,000; Full time

Benefits offered by this job

Equity package

Learning and development budget

Health and wellbeing allowance

High ownership and leadership exposure

Job summary

Harnham invites a Senior Machine Learning Scientist to join our London team, focusing on cutting-edge NLP and GenAI. You will lead development of NLP/LLM solutions, design end-to-end experiments, and partner with engineering to deploy production-ready models in a fast-paced product environment.

You will work across information extraction, semantic understanding, and intelligent automation, shaping model training pipelines and evaluation frameworks while communicating insights to non-technical

Qualifications

• Strong commercial experience in ML, NLP, and LLMs.
• Hands-on fine-tuning and evaluation of language models.
• Experience with large-scale, unstructured text datasets.
• Excellent communication and collaboration across teams.
• CS or related technical background; postgraduate study favoured.

Responsibilities

• Develop, evaluate, and optimise NLP and LLM solutions.
• Design and run end-to-end experiments to benchmark performance.
• Collaborate with Product and Engineering to deploy research.
• Translate business challenges into scalable ML solutions.
• Influence technical direction through experimentation and innovation.
• Build GenAI features and agentic AI workflows.

Skills

NLP

LLMs

Transformer architectures

Machine Learning

Education

Postgraduate study in CS or related field

Tools

Python

PyTorch

Job description

Harnham invites a Senior Machine Learning Scientist to join our London team, focusing on cutting-edge NLP and GenAI. You will lead development of NLP/LLM solutions, design end-to-end experiments, and partner with engineering to deploy production-ready models in a fast-paced product environment.

You will work across information extraction, semantic understanding, and intelligent automation, shaping model training pipelines and evaluation frameworks while communicating insights to non-technical