3 of 3 Reinforcement Learning Jobs in the City of London

An exceptional opportunity for a Machine Learning Engineer (with Full-Stack experience) to join an innovative market leader at the forefront of developing next-generation solutions that transform digital interactions. The role will focus on projects to leverage state-of-the-art generative AI, retrieval-augmented generation (RAG), and … reasoning frameworks to build intelligent and context-aware systems. We are seeking talented Machine Learning Engineers with full-stack software development experience to join our client's team and help shape the future of AI-powered automation. Within this dynamic role varied duties will include: Search relevancy engineering. Conversational … pipelines for effective information retrieval from structured and unstructured sources. Model Fine-Tuning & Training : Train domain-specific models using techniques like LoRA, QLoRA, PEFT, reinforcement learning, and supervised fine-tuning (SFT). Model Deployment & Inferencing : Optimise model serving and inference using vLLM, DeepSpeed, TensorRT, Triton, and other acceleration More ❯

of the following areas: Artificial Intelligence: Experience with state-of-the-art methods such as graph neural networks, transformers, Gaussian processes, generative modeling, or reinforcement learning. Cheminformatics: Knowledge of chemistry data storage, formats, and synthesis prediction; proficiency with toolkits such as RDKit or OpenEye. Quantum Mechanics: Experience applying QM … processing from heterogeneous sources; familiarity with tools like Apache Spark or Hadoop. Proficiency with cloud platforms (AWS, GCP, Azure). Familiarity with major machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Open-source contributions or publications demonstrating expertise in machine learning for scientific applications. Hands-on experience More ❯

of the following areas: Artificial Intelligence: Experience with state-of-the-art methods such as graph neural networks, transformers, Gaussian processes, generative modeling, or reinforcement learning. Cheminformatics: Knowledge of chemistry data storage, formats, and synthesis prediction; proficiency with toolkits such as RDKit or OpenEye. Quantum Mechanics: Experience applying QM … processing from heterogeneous sources; familiarity with tools like Apache Spark or Hadoop. Proficiency with cloud platforms (AWS, GCP, Azure). Familiarity with major machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Open-source contributions or publications demonstrating expertise in machine learning for scientific applications. Hands-on experience More ❯