Artificial Intelligence Jobs

Faculty · London

Sets technical direction for complex, business-critical AI projects in the life sciences team, architecting production-grade machine learning solutions and overseeing end-to-end delivery. Balances speed, innovation and reliability while leading technical scoping for high-value sales opportunities and mentoring data scientists across the wider craft.

Faculty · Remote (UK)

Sets technical direction for complex, business-critical AI projects in the defence sector, architecting production-grade machine learning solutions and overseeing end-to-end delivery. Balances speed, innovation and reliability while mentoring data scientists and acting as a trusted technical advisor to demanding clients. Requires UK Security Clearance eligibility and 2-4 days per week on-site with customers.

Capital One · Lincoln

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. The role involves setting technical direction, mentoring junior team members, and driving prototypes through to production in collaboration with business stakeholders.

Capital One · Mansfield

Lead the development of proprietary deep learning models to enhance underwriting capabilities, building neural networks such as LSTMs, RNNs and Transformers on structured and unstructured data. Set technical direction, manage projects, communicate with stakeholders and mentor junior team members, taking ideas from prototype to production.

eFinancialCareers · London

Leads a global legal and professional services firm's AI and data agenda, translating strategy into a delivery programme that moves initiatives from experimentation to sustainable adoption. Works across legal, fiduciary and business services divisions on productivity, decision-making and client outcomes, reporting to the Director of Service Innovation and managing AI and data delivery teams through matrix leadership.

Databricks · London

Customer-facing role building and productionising data and AI solutions on the Databricks platform, owning architecture and design decisions for end-to-end systems spanning data engineering, ML/AI and application development. Embeds with client teams from engineers to executives, delivering production-grade reference architectures and reusable accelerators, with around 20% travel.

Databricks · London

Drives large-scale enterprise data warehouse migrations onto the Databricks Intelligence Platform across EMEA, partnering with GTM leadership, product specialists and account teams to shape adoption strategy. Acts as the tier-3 escalation point for complex or stalled migrations, builds assessment tooling and programmatic migration assets, and mentors the regional Solutions Architect community. Also feeds field insights back to Product and Engineering and authors whitepapers and conference talks.

CrowdStrike · United Kingdom

Lead a team of three data scientists at the intersection of machine learning, big data and cybersecurity, setting technical standards and driving strategy for threat detection and agentic AI systems. Work spans research through production deployment, using CrowdStrike's large-scale behavioural and malware data, while mentoring the team and partnering with security research, engineering and product.

CrowdStrike · Spain

Leads a team of three data scientists building machine learning and agentic AI systems for threat detection, malware analysis and security automation. Owns technical strategy, roadmaps and production deployment while mentoring staff and partnering with security research, engineering and product teams.

Experis · United Kingdom · £400 – £500 pd

Multiple contract roles spanning data, ML and AI platform engineering, from hands-on specialists to technical leads. Work covers architecture, engineering, governance, security, reliability and operations on a large-scale modern environment, using Python, SQL, Go, DataHub, Trino/Starburst, JupyterHub, OpenSearch, Entra ID/OIDC, FluxCD/Argo CD and Helm. Remote and global, with rates negotiable by experience and location.

Wayve · London

Post-train, distill and evaluate GAIA-family large video diffusion world models for synthetic driving data, covering embodiment and pose transfer. Own large-scale GPU generation pipelines and land synthetic data into the driving-model training stack, working between ML research and platform engineering.

Wayve · London

Owns the data strategy behind an end-to-end autonomous driving AI platform, spanning the full learning cycle from foundation model training to production programmes. Leads a multidisciplinary data organisation, deciding what data matters and building the flywheel from real-world signals through discovery, curation and enrichment. Focuses on finding rare, safety-critical scenarios in very large datasets using embeddings, semantic search and model-driven selection.

Amazon · London

Acts as a trusted technical advisor to AGI-DS scientists, translating complex research objectives into actionable ML/GenAI data collection requirements. Coordinates across teams to ensure delivery, owns customer scientist relationships, and monitors collection health metrics. Involves guiding on workflow feasibility, annotation scalability, and data collection best practices.

University of Warwick · Coventry · £21,805 pa

Develops medium-voltage solid-state transformer cells for hyperscale AI data centres and net-zero grids, tackling high-frequency power conversion, compact magnetic links and electrical-thermal degradation under high-dv/dt stress. Combines multi-physics finite-element modelling in ANSYS/COMSOL with experimental validation in a high-voltage power electronics lab, culminating in a scaled hardware prototype.

eFinancialCareers · London

Leads the design, development and operation of high-rigor analytical and machine-learning systems across a complex, regulated financial-services estate, defining the AI/ML roadmap while remaining hands-on. Owns the full model lifecycle from feature engineering and back-testing to deployment, monitoring and incident response, with models expected to be explainable and production-grade. Works closely with engineering, data platform and product teams, mentoring other data scientists with limited line management.