Top Research Jobs in London

Develop and maintain Python and SQL research tools, analytics, data pipelines, and AI-enabled workflows. Analyze and validate large financial datasets, investigate anomalies, support quantitative investment and index research, and apply regression, optimization, factor analysis, risk modeling, and performance analytics. Collaborate with research, engineering, technology, and data operations teams to deliver scalable solutions, support index methodologies and rebalances, resolve analytical issues, and contribute to client-facing analysis.

Leads complex, large-scale quantitative research programmes across multiple countries, overseeing methodology, sampling, questionnaire design, fieldwork, reporting, budgets, risks, quality assurance, and dissemination. The role manages stakeholders, suppliers, multidisciplinary teams, and client relationships; develops proposals and business opportunities; presents findings to senior audiences; supports methodological development; and coaches colleagues. Work focuses on international social, health, human rights, migration, education, employment, and economic development research.

Develop research methods for modeling, optimization, and control of complex industrial systems. Formulate research questions from live plant challenges, design rigorous experiments, incorporate physics and operating constraints, collaborate with engineering teams to deploy machine learning and control algorithms, evaluate production performance, investigate failures, and communicate findings. Focus areas include system identification, Bayesian optimization, probabilistic modeling, physics-informed machine learning, active learning, and model-based reinforcement learning.

Lead full-cycle recruitment for niche AI/ML, computational, scientific, and deep-tech roles. Source passive candidates, manage complex global interview processes, advise hiring managers, support Programme Director searches, and build scalable recruiting systems. Partner with Communications on employer branding and represent ARIA at scientific and technology events to develop talent pipelines. The role requires strong in-house recruitment experience, proactive sourcing, autonomy, organization, systems thinking, and familiarity with the UK R&D talent landscape.

Conduct research on 3D generation, reconstruction, and world models. Design, implement, and evaluate state-of-the-art methods and prototypes, collaborate with the research team, present findings, and contribute to publications at leading computer vision, graphics, and machine learning conferences.

Leads end-to-end scientific and human research strategy across innovation and new product development. Translates complex research into actionable insights, guides testing and evidence generation, builds research partnerships and facilities, and strengthens research capabilities across the organization. The role supports evidence-based, consumer-first innovation from early opportunity exploration through market-ready concept validation.

Research and develop generative AI algorithms for multimodal understanding, image and video generation, and 3D content generation. Explore large-model applications in game development and collaborate on project development, testing, and deployment.

Mercor seeks research proposals examining AI’s economic and labor impacts, human-AI collaboration, productivity, enterprise adoption, and returns. Eligible applicants include researchers at universities, nonprofits, public benefit corporations, and small startups in the United States or United Kingdom. Applicants must submit an expression of interest describing methodology, team expertise, outcomes, timeline, risks, and budget. Selected projects must produce an openly licensed paper, dataset, methodology, or tool.

Develop algorithms and production systems for world-scale geospatial registration, 3D geometry, correspondence, mapping, and learned 3D reasoning. Build datasets, pipelines, benchmarks, and evaluation methods using imagery, point clouds, LiDAR, GPS, and geographic data. Apply computer vision, machine learning, transformers, and geometric optimization to improve a living world map, while collaborating across research, engineering, and product teams and communicating results through technical documentation and presentations.

Build environments, agents, evaluations, reward systems, and post-training methods for quantitative research and systematic decision-making. Develop realistic market and portfolio simulations, train long-horizon research agents, assess out-of-sample robustness, and create production feedback loops with monitoring and failure attribution. Collaborate directly with customers and research partners while maintaining a substantive research agenda.

Build post-training and agent systems for public-sector and regulated environments. Develop private improvement loops, permissioned tool-using agents, high-assurance evaluations, and deployable model updates within strict data and infrastructure boundaries. Conduct experiments, inspect traces, establish meaningful baselines, attribute failures, and collaborate directly with customers and research partners. The role requires hands-on model-training or agent experimentation, strong Python skills, sound research judgment, and comfort solving ambiguous problems.

Build environments, evaluations, agent systems, and post-training methods for embodied robotics in collaboration with an external robotics team. Train and evaluate agents using reinforcement learning, imitation learning, world modeling, planning, simulation, and multimodal perception. Own long-horizon agents, sim-to-real evaluations, reward systems, and training loops using offline and online feedback while accounting for safety and hardware constraints.

Build post-training and evaluation systems for multimodal computer-use agents operating browsers, desktops, and cross-application workflows. Develop long-horizon environments, reward signals, benchmarks, recovery strategies, and secure sandboxes using real workflows and production traces. Run experiments, inspect failures, attribute performance gains, and deploy successful research. The role involves external collaboration with customers, users, and research partners, requiring strong Python skills and ownership of ambiguous research problems.

Own externally facing applied reinforcement learning programs from ambiguous customer problems through deployment. Responsibilities include defining environments, evaluations, baselines, and reward signals; running post-training and distillation experiments; building agents with tool use and long-horizon capabilities; diagnosing model, data, harness, inference, and infrastructure failures; measuring customer impact; and converting successful work into reusable research, open-source systems, and platform capabilities.

Build agentic reinforcement-learning systems for semiconductor design and verification workflows. Develop interactive EDA environments, verifiers, reward functions, evaluations, and post-training experiments. Agents will generate or debug RTL, invoke simulation and formal tools, interpret failures, and optimize for correctness, efficiency, power, performance, and area. The role involves externally facing research, experimentation, failure analysis, and collaboration with customers, users, and research partners.

Conduct applied research on reinforcement learning for long-horizon, tool-using, multi-agent systems. Develop agentic judges, reward and credit-assignment methods, environments, evaluations, and scalable experimental systems. Diagnose reward hacking, data leakage, and infrastructure artifacts through rigorous experimentation. Build reusable open-source systems and publish findings while collaborating with customers, application teams, research partners, and open-source users.

Build continual learning systems that convert agent trajectories, corrections, and failures into training signals and model updates. Develop and evaluate streaming reinforcement learning, post-training, distillation, memory, and adaptation methods. Create reproducible task-generation, training, evaluation, regression, and rollback pipelines for changing task distributions. Conduct field experiments with external collaborators, assess retention and transfer, and establish promotion gates and human oversight for continuously improving production agents.

Own the development of open-source reinforcement learning, evaluation, agent, and environment systems. Build researcher-facing platform features, stable APIs, reference implementations, and technical documentation. Collaborate with engineers, customers, research partners, and external contributors to turn experimental research into reliable products and community contributions. Conduct model-training and post-training experiments, inspect traces, establish meaningful baselines, attribute failures, and improve workflows for researchers building and deploying advanced AI systems.

Conduct remote and on-site monitoring of oncology clinical trial sites, ensuring protocol compliance, patient safety, data integrity, informed consent quality, regulatory compliance, and inspection readiness. Review source data, investigational product records, essential documents, and protocol deviations; resolve queries and document monitoring findings. Collaborate with investigators, site personnel, sponsors, and project teams to support study delivery, timelines, and corrective actions.

Conduct quantitative research on financial markets by discovering predictive patterns, developing statistical and machine learning models, running large-scale simulations and backtests, and translating research into production-ready trading signals. Interns collaborate with researchers and technologists, build research infrastructure and AI-powered agents, and complete rigorous projects from hypothesis through evaluation under realistic market conditions.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Executes event marketing campaigns across email, social media, websites, and digital platforms to generate leads, registrations, and revenue. Responsibilities include building marketing plans, creating and testing campaigns, managing audience segmentation, analyzing performance and ROI, maintaining budgets, coordinating partnerships and event activities, and reporting results. The role also mentors marketing assistants, conducts event interviews, supports on-site operations, and develops specialized knowledge of event audiences and brands.

Leads Ofcom’s research innovation and data strategy, overseeing statistics, analytics, behavioural research, innovative research methods, and programme management. The role develops responsible data and AI capabilities, improves access to insight, strengthens cross-organisational collaboration, manages senior teams and budgets, and represents Ofcom externally. It requires strategic leadership, advanced data science and research expertise, data ethics knowledge, programme management, change leadership, and the ability to communicate complex analysis to regulatory, policy, commercial, and external stakeholders.

Evaluate and onboard macro financial datasets for quantitative research and trading. Assess data coverage, quality, consistency, APIs, and delivery mechanisms; develop Python tools and pipelines to extract, clean, normalize, and analyze data; investigate discrepancies across providers; improve dataset quality; and collaborate with researchers, traders, vendors, and engineering teams to automate reliable data integration.