> Markdown version of [/jobs/ext/3642815-senior-machine-learning-engineer-care](https://www.wearedevelopers.com/jobs/ext/3642815-senior-machine-learning-engineer-care). 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). --- # Senior Machine Learning Engineer - Care - **Company:** Deliveroo - **Location:** London, UK - **Experience:** Expert - **Salary:** £58,028.0 - **Contract:** Permanent contract - **Skills:** Python (Programming Language), Machine Learning, Circleci, Large Language Models, Generative AI, Agentic-AI, Kubernetes, Infrastructure Automation Frameworks, Production Code, Machine Learning Operations, Docker - **Published:** October 9, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5918844010 ## About the Role * Significant experience as an ML Engineer or Data Scientist, with a proven ability to write high-quality production code in Python. * Demonstrated ownership of productionising Generative AI workstreams or Agentic AI projects, including a deep understanding of LLMs, transformers, and fine-tuning. * Proven ability to deploy and manage models using modern infrastructure tools such as Docker, Kubernetes, and CircleCI. * Strong foundational knowledge of traditional ML and evaluation techniques, balanced with a bias for simplicity and measurable business impact. * A collaborative mindset with experience mentoring peers and a drive to solve complex, real-world problems at scale. Nice to Have * Experience with evaluation harnesses and frameworks specifically designed for Generative AI. * Familiarity with observability, monitoring, and safety techniques for deployed GenAI systems. * Experience working with strongly typed languages, such as Go. ## Description You'll be joining the Care team. Care is an area with significant opportunity and real customer impact; you'll take ownership of existing models and help define strong ML foundations in spaces that are still evolving, from LLM-powered automation to customer compensation logic. Here's what your day-to-day might look like: * Own the full ML lifecycle: Lead the design, development, and productionisation of machine learning models used in customer support and high-stakes decision systems. * Architect for reliability: Build robust monitoring, evaluation, and alerting frameworks to detect model underperformance, drift, or unexpected behaviour in real-time. * Collaborate on product strategy: Partner closely with Product Managers to turn ambiguous customer problems into robust, scalable ML solutions. * Drive technical excellence: Provide technical leadership in areas with unclear ownership, setting the gold standard for ML quality, reliability, and maintainability. * Bridge science and engineering: Work with data scientists and engineers across the global organisation to embed models into high-performance, scalable systems.