4 of 4 Permanent Experimental Design Jobs in the City of London

Information Retrieval (Research-Focused) Drive applied research within a production-focused Information Retrieval team. You will: Research and improve retrieval and NLP models. Design experiments, run evaluations, and iterate on modelling approaches. Collaborate closely with engineering to transition research into production. You bring: Strong applied research experience ...

Partner with Product, Data, and Engineering leaders to shape and deliver an actionable ML strategy that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure … rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy. Excellent communication and stakeholder management skills ...

Senior ML Engineer to join a leading organisation delivering cutting-edge AI and machine learning solutions with real-world impact. You will design, develop, and deploy ML models, including traditional ML and GenAI/LLM applications, supporting national security and regulated sector objectives. This is a London-based … role with hybrid working support. Key Responsibilities: Design, develop, and iterate ML models for forecasting, classification, anomaly detection, and GenAI/LLM applications. Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly. Transition validated experiments into production-ready solutions, collaborating with engineers ...

client are looking for AI Researchers specialising in Reinforcement Learning with Human Feedback (RLHF) and Generative AI. In this role, you will design and optimise the algorithms that align large-scale generative models with human preferences, ensuring they are safe, controllable, and capable of producing high-quality outputs … learning methods (policy gradients, actor-critic, off-policy learning) for model alignment. Train, fine-tune, and evaluate LLMs and diffusion models at scale. Design experiments to align generative outputs with human and organisational preferences. Collaborate with researchers, engineers, and human feedback teams to build scalable alignment pipelines. Publish ...