3 of 3 Remote/Hybrid Reinforcement Learning Jobs in Central London

permanent position with candidates required to do hybrid working in either Cambridge or London. Our 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 … build the next generation of foundation models Responsibilities: Develop and refine RLHF algorithms for large language and generative models. Research and implement deep reinforcement learning methods (policy gradients, actor-critic, off-policy learning) for model alignment. Train, fine-tune, and evaluate LLMs and diffusion models at scale. ...

Responsibilities Modelling and Forecasting Develop time‐series models for generation, consumption, and market price forecasting. Build probabilistic and scenario‐based forecasting capabilities. Apply machine learning to optimise matching, pairing, and routing algorithms within the P2P marketplace. Trading and Optimisation Intelligence Create algorithms that optimise buyer–seller matching, pricing … learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer, reinforcement learning desirable). Strong SQL and practical experience with production‐ready data pipelines. Experience working with cloud environments (AWS, GCP, or Azure). ...

Title: AI Architect (Wealth) Job Description This position is pivotal in designing AI and Machine Learning solutions on cloud-based platforms, exploring emerging AI trends, developing proof-of-concepts, and collaborating with internal and external ecosystems to advance these concepts to production. The role demands expertise in designing … least 6-10 years of hands-on development and architectural experience. Proficiency in Python, PyTorch, TensorFlow, or similar frameworks. experience with supervised, unsupervised, and reinforcement learning. Solid grounding in Natural Language Processing (NLP) concepts such as tokenisation, embeddings, semantic search, text classification, and summarisation. Strong understanding of Large Language ...