> Markdown version of [/jobs/ext/808703-remote-senior-applied-machine-learning-engineer-catalogue-intelligence](https://www.wearedevelopers.com/jobs/ext/808703-remote-senior-applied-machine-learning-engineer-catalogue-intelligence). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Remote Senior Applied Machine Learning Engineer - Catalogue Intelligence - **Company:** Onbuy Limited - **Location:** Reading, UK - **Experience:** Expert - **Salary:** £65,000.0 - £75,000.0 - **Contract:** Permanent contract - **Skills:** BigQuery, Information Extraction, Python (Programming Language), Machine Learning, Operational Databases, Standard Sql, SQL Databases, Unstructured Data, Large Language Models, Semi-structured Data, Data Analytics, Machine Learning Operations, Data Pipelines - **Published:** June 23, 2026 - **Apply:** https://jobsthamesvalley.co.uk/jobs/remote-senior-applied-machine-learning-engineer-catalogue-intelligence-reading-berkshire/2835158805-2/ ## About the Role * Experience building and shipping production data or ML systems with measurable business impact * Strong Python and SQL skills, with the ability to work across data pipelines end-to-end * You should be comfortable applying modern approaches such as LLMs, multimodal models, and information extraction techniques, and taking them from experimentation into production with proper evaluation, monitoring, and cost control. * Experience working with messy, unstructured or semi-structured data (e.g. text, images, product data) * Ability to design systems that make decisions, not just predictions * Strong judgement in balancing accuracy, risk, and business impact * Experience with ecommerce or marketplace catalogues is a plus, but not required. ## Description We're building a more intelligent, scalable product catalogue across multiple markets. Core capabilities like auto-categorisation and brand detection already exist, but they are not yet connected into a system that consistently drives quality, discovery, and growth. This role owns that system. The Senior Applied ML Engineer - Catalogue Intelligence is responsible for building the machine learning systems that power OnBuy's catalogue decisioning engine. Working in partnership with the Head of Seller Solutions, who defines catalogue rules and commercial logic, you will design and deploy production-grade systems that automatically improve: * Product categorisation * Product data quality and completeness * Pricing competitiveness insights * Catalogue coverage and selection * Product discoverability This is a hands-on, production-focused role where outputs directly modify the live catalogue and materially impact GMV, conversion performance, search, and discovery. Core mission Turn catalogue rules and commercial logic into automated, data-driven systems that continuously improve discovery, data quality, pricing competitiveness, and revenue outcomes. What you'll be responsible for You'll take ownership of how product data is structured, validated, and used across the platform. This includes: * Improving how we classify and understand products at scale * Raising the overall quality of catalogue data and defining what good looks like * Ensuring product data supports effective search, filtering, and discovery * Identifying gaps in our catalogue and surfacing opportunities for growth * Improving how our catalogue performs across external channels * You'll build and evolve the systems and decision logic that enable this, and iterate based on real-world performance and data. You'll work across: * Structured data (catalogue attributes, GTINs, taxonomy) * Unstructured data (text and images) * Behavioural data (search, clicks, conversions) How you'll work You'll build directly using SQL and Python on top of: BigQuery Airbyte Google Datastream You'll be working across data pipelines, information extraction, and production ML systems, combining rules, heuristics, and ML/LLMs where appropriate. The focus is on shipping practical systems quickly, validating them with real data, and improving them over time. 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