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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Systems Engineer - **Company:** Flawless Holdings - **Location:** London, UK - **Experience:** Expert - **Salary:** £57,000.0 - £97,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Automated Storage and Retrieval Systems, Big Data, Extract Transform Load (ETL), Software Debugging, Distributed Systems, Python (Programming Language), Machine Learning, Node.Js, Tensorflow, Azure Machine Learning, Search Technologies, Data Storage Technologies, Pytorch, ReactJS, Backend, Build Management, Data Lakes, Data Management, Machine Learning Operations, Front End Software Development, Data Pipelines - **Published:** September 11, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5878312765 ## About the Role * We are looking for experienced ML Systems Engineers, open from Senior Engineer through Staff Engineer levels. * We value experience building machine learning infrastructure, ML platforms, data platforms, or large-scale backend systems. * We want strong Python engineering skills and experience building production services. * We look for a deep understanding of data pipelines and performance trade-offs across storage, networking, memory, and compute. * We expect hands-on experience with machine learning frameworks such as PyTorch. * We value experience building and operating distributed systems. * We look for experience working with large-scale datasets and high-throughput data processing pipelines. * We prefer familiarity with modern data storage and analytics technologies, including columnar data formats and data lake architectures. * We need strong debugging, problem-solving, and systems design skills. * We value effective collaboration with cross-functional teams. * For Staff Engineers, we expect technical leadership across significant infrastructure initiatives, architecture and technical strategy experience, influence beyond an individual team, mentoring ability, and a track record of balancing immediate research needs with long-term platform investments. * Nice to have: experience with video, media, or multimodal machine learning pipelines; embeddings, vector search, or retrieval systems; production inference systems; and frontend experience with React or similar for internal tools and workflows. ## Description * We will have you build and evolve data platforms used to curate and manage large-scale multimodal datasets. * We will have you design systems that index, process, and enrich thousands of videos through machine learning pipelines. * We will have you optimize data storage and access patterns for efficient model training and experimentation. * We will have you improve reliability, scalability, and observability across the data ecosystem. * We will have you build and optimize infrastructure for large-scale model training. * We will have you improve performance across single-node and distributed training environments. * We will have you scale data loading, preprocessing, and training workflows. * We will have you ensure training pipelines are reproducible, efficient, and easy to operate. * We will have you develop systems for collecting, storing, and analyzing model outputs. * We will have you build tooling for dataset exploration, experiment tracking, and model comparison. * We will have you enable scientists to iterate rapidly while maintaining robust evaluation practices. * We will have you design and maintain infrastructure for model versioning, experimentation, validation, and deployment. * We will have you improve reproducibility and governance across the machine learning lifecycle. * We will have you support the promotion of models from research through production. * We will have you build and optimize inference infrastructure for production workloads. * We will have you define and improve model serving protocols and deployment patterns. * We will have you enhance performance, reliability, and scalability of production inference systems. * We will have you work closely with scientists, machine learning engineers, and platform teams to design and build the systems that underpin model development and deployment. * Senior candidates will provide technical leadership, drive architectural decisions, mentor other engineers, and influence infrastructure strategy across multiple initiatives. 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