Senior Machine Learning Scientist

Job Description

<p>Metric has partnered with a VC-backed AI company working with leading retail and ecommerce brands to improve how their product data performs across search engines, generative answer engines, and AI-driven discovery systems.</p><p><br></p><p>They are looking for a Senior Machine Learning Scientist specializing in Search Engine Optimisation and Generative Engine Optimisation, applied through data, modelling, and experimentation.</p><p><br></p><p><b>What you will work on:</b></p><ul><li>Building algorithms that improve product visibility in generative search and answer engines</li><li>Analysing search, clickstream, and system data to understand AI crawling and summarisation</li><li>Applying NLP to model intent, relevance, and semantic signals used by LLMs</li><li>Predicting the impact of technical optimisation changes on generative search performance</li><li>Automating structured data and factual enrichment with engineering teams</li><li>Running experiments to measure GEO and AEO impact versus traditional search</li></ul><p><br></p><p><b>What they are looking for:</b></p><ul><li>6+ years experience in Machine Learning, Applied Science, or Data Science</li><li>Hands-on experience with NLP, LLMs, embeddings, and semantic search</li><li>Understanding of search relevance and information retrieval concepts</li><li>Experience working with large datasets using SQL and cloud data platforms</li><li>Strong Python skills for data analysis (Pandas, NumPy) and ML (Scikit-learn, PyTorch/TensorFlow)</li><li>Familiarity with SEO or search data APIs for programmatic analysis</li></ul><p><br></p><p><b>Preferred skills:</b></p><ul><li>Experience optimising for generative or answer-based search systems (GEO/AEO)</li><li>Experience running controlled experiments or A/B tests in search environments</li><li>Experience working in a startup or high-growth product environment</li></ul><p><br></p><p>This role is a great fit for someone who has already worked on SEO or search optimisation problems, and has taken a scientific, ML-led approach to improving visibility, attribution, and retrieval performance.</p>