Top Research Jobs in London

Build and maintain experimental decision-system prototypes, evaluation tooling, simulation environments, and research infrastructure to enable rapid iteration and validation of research concepts in real-world constraints.

Apply formal decision theory and structured reasoning to enterprise problems: design decision frameworks, model uncertainty and tradeoffs, test decision architectures, run experiments, and translate research into system-ready primitives collaborating with engineering and product teams.

Lead go-to-market strategy and commercial execution for a new AI-native Market Research service. Build positioning, pricing, and metrics; generate pipeline and close deals; partner with Sales, Marketing, Product, and Research; represent Maze publicly; and help scale the service and team while feeding market insights back into product and offering design.

Develop and train advanced LLMs and VLMs, optimize training methods, design data pipelines, and evaluate model performance. Collaborate with teams in AI systems integration.

Develop and train advanced large language models and vision-language models, implement training methods, design data pipelines, and collaborate with teams to integrate models into AI systems.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

Join Tether as an AI Research Engineer focusing on model compression and quantization for AI systems. You'll innovate in reducing model footprint for multimodal architectures while applying advanced compression techniques, ensuring high performance on edge devices.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead and deliver international healthcare market research projects across the full lifecycle: planning, setup, fieldwork, quality control, vendor management, client reporting, budget ownership, and mentoring junior staff while ensuring ISO-compliant processes and client satisfaction.

Develop computer vision models for object recognition and video understanding while evaluating and improving their performance for real-world applications.

The Engineering Manager will lead a high-performing team, define technical strategy, evolve research technology, and enhance research output through efficient innovations.

Design, build, and operate Kubernetes infrastructure for high-demand workloads, ensuring security, reliability, and scalability while collaborating with research and trading teams.

As a Research Engineer, you'll improve RL training infrastructure, identify bottlenecks, collaborate with teams, and enhance reliability of research runs.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Volunteer role supporting communications: research and write monthly newsletters, create social media content, proofread publications, draft press releases and blog posts, support fundraising promotion, research gender and development news, and propose marketing ideas. Requires ~10–15 hours/week and reports to the Director of Communications.

Lead end-to-end delivery of multiple trading and enterprise infrastructure projects, including colocation site builds, datacenter provisioning, exchange connectivity, and compute allocation. Coordinate with networking, data centre operations, vendors, brokers and exchanges, track progress in JIRA, and ensure resilient, high-performance platforms delivered on time and to quality standards.

Lead research on designing, building, training, and optimizing diffusion-based generative models for high-quality 3D content from diverse inputs, emphasizing world-scale scenes.

Design, build and maintain production-grade Python services and scalable APIs supporting risk analytics. Collaborate with Risk Managers and Quants to productionise models, ensure performance, reliability, observability, and improve engineering standards using cloud-native practices and AI-assisted tools.

Analyze large financial datasets, validate and develop automated models, model corporate actions and index changes, write client-facing research reports, communicate insights to clients, and source public information.

Support development of tokenized equity products by conducting due diligence, building financial and commercial models, performing market and competitor research, preparing committee memos and presentations, tracking ECM activity, and owning research workstreams through transaction execution.

The Quantitative Research Analyst will research methods to improve trading systems, develop new trading signals, and optimize portfolio performance through quantitative analysis.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.