Edward Killin

Machine Learning Research Engineer Location: London (Hybrid) Job Type: Full-Time Salary: £80,000 - 100,000 We're partnering with an innovative deep-tech organisation developing next-generation computing technology and advanced AI solutions. As the company continues to expand its research capabilities, they're looking to appoint a Machine Learning Research Engineer to help drive the development of novel machine learning algorithms and applications. This is a rare opportunity to work on cutting-edge technology at the intersection of machine learning, advanced computing, and emerging hardware platforms, helping to solve complex real-world challenges for customers and partners worldwide.  The Opportunity As a Machine Learning Research Engineer, you'll develop and benchmark new machine learning approaches, with a particular focus on generative AI and advanced neural network architectures. You'll work alongside a multidisciplinary team of researchers and engineers, translating innovative ideas into practical solutions and customer-facing applications. Key ResponsibilitiesDevelop, test and benchmark advanced machine learning algorithms.Work with generative AI models including diffusion models, flow models and GANs. Design and evaluate novel hybrid machine learning architectures.Collaborate with customers and strategic partners to identify and solve complex technical challenges.Contribute to software products through the development of new algorithms, tools and examples. Support research activities through experimentation, analysis and publication of results. Contribute to the creation and protection of intellectual property arising from your work. Essential RequirementsProven experience developing and benchmarking machine learning algorithms. Hands-on experience with diffusion models, flow models and/or GANs. Strong programming skills in Python and PyTorch. Experience using version control systems such as Git. MSc or PhD in Computer Science, Machine Learning, Physics, Mathematics or a related discipline. Excellent communication skills and the ability to work effectively within multidisciplinary teams. Desirable ExperienceExperience working with advanced computing infrastructure including HPC, GPUs, NPUs, ASICs or other specialist hardware.Experience training and optimising multi-GPU models. Published research in machine learning or a related field. Experience working directly with customers or external stakeholders. An interest in or understanding of quantum computing technologies. What's On OfferThe opportunity to work on genuinely innovative technology at the forefront of AI and advanced computing.A collaborative environment alongside highly experienced researchers and engineers.Exposure to cutting-edge machine learning research and commercial applications.Competitive salary and benefits package.Hybrid working based in London.

Principal Software Engineer (AI)Our work focuses on delivering innovative technology that creates meaningful, real-world impact. We are united by a shared commitment to developing solutions that help organisations harness the power of data, AI, and advanced software capabilities.We help life sciences and BioPharma organisations unlock the potential of AI and machine learning to improve outcomes and accelerate innovation. Through cloud-based software platforms and specialist services, we support organisations in managing complex scientific and operational data, improving workflows, and enabling smarter decision-making.With decades of expertise in scientific informatics, our solutions help organisations design, execute, and optimise processes, manage and structure data, and generate valuable insights across the product lifecycle—from research and development through to manufacturing.The Principal Software Engineer (AI) will provide technical leadership in the design and delivery of production-grade, GenAI-enabled capabilities across enterprise software platforms. The role focuses on applying large language models (LLMs) to real-world enterprise data, including scientific, clinical, and operational information, to enable AI-driven discoverability, summarisation, reporting, and decision support.Operating at principal level, you will be hands-on, guiding engineering teams through the transition from proof of concept (POC) to early adoption and general release, while helping establish sustainable AI engineering practices across the wider technology organisation.You will collaborate closely with data scientists, domain specialists, and technical teams to deliver intelligent solutions for knowledge extraction, decision support, and workflow automation.In this role, you will have the opportunity to:Lead the delivery of AI-enabled platform capabilities that accelerate insight generation and decision-making across complex enterprise environments.Design and productionise scalable GenAI solutions using approaches such as retrieval augmented generation (RAG), integration frameworks, and selectively applied agentic patterns—balancing capability, cost, performance, and trust.Translate scientific and business requirements into robust AI system designs that integrate effectively with data platforms, workflows, and APIs.Establish engineering best practices for GenAI and LLM applications, including architecture patterns, evaluation approaches, reliability, scalability, and maintainability standards across the AI engineering lifecycle.Act as a technical leader by mentoring engineers, influencing architectural decisions, and supporting successful delivery and adoption of AI capabilities across software platforms.Essential requirements:Experience designing, building, and operating LLM-powered capabilities such as document summarisation, retrieval augmented generation (RAG), and question-answering systems over enterprise datasets.Experience developing AI-ready data foundations, including unstructured data pipelines, metadata extraction and enrichment, and hybrid architectures using vector stores alongside relational databases.Proven ability to deploy AI systems into production environments, including monitoring model behaviour, data drift, cost, and latency.Strong judgment in making pragmatic engineering trade-offs to support reliability, scalability, and enterprise adoption.Experience establishing robust AI operational practices, including versioning of prompts, models, and data, as well as implementing feature flags and controlled rollout strategies.Ability to collaborate effectively with cross-functional partners, provide technical leadership, mentor engineers, and drive delivery in complex environments.Desirable (but not essential):Experience applying GenAI solutions within life sciences, healthcare, scientific, clinical, or regulated environments.Familiarity with building AI capabilities that support knowledge extraction, decision support, and workflow automation across enterprise data ecosystems.Experience with cloud-based platforms and modern data technologies such as Databricks and AWS.Experience defining or contributing to organisational best practices for AI engineering, including evaluation methods, governance frameworks, and scalable adoption approaches for generative and agentic AI.