Artificial Intelligence Jobs

Faculty · London

Conducts original technical research in AI safety, designing experiments to study and steer transformer-based LLM behaviour using white-box and black-box interpretability methods. Builds reproducible research code and tooling in Python and PyTorch, and communicates findings to frontier labs, government bodies and safety institutes. Works alongside senior and lead scientists, with part-time hours open to discussion.

Faculty · London

Lead the design, build and deployment of production-grade ML and software systems for clients across pharma, academia and MedTech. The role blends hands-on Python and cloud engineering with architecture decisions, client-facing advisory work and mentoring junior engineers. It sits within a cross-functional life sciences team focused on AI for therapy research and commercialisation.

Wayve · London

Develops and maintains Wayve's Core Simulation system in modern C++ and Python, integrating driving-stack components, simulated robot systems and vehicle-platform capabilities. Initially focused on high-scale open-loop evaluation, the role involves debugging on-road versus simulated behaviour, improving reproducibility, observability and automated testing, and profiling simulator performance across a shared codebase.

Wayve · London

Sets technical strategy and roadmap for fault detection and fallback systems in an autonomous driving stack, designing and training detection mechanisms for robust driverless operation. Leads integration into the shared driving stack, aligning ML, inference optimisation and software teams, and mentors through architecture reviews. Works with large-scale training and fleet data in a small, high-impact team.

Capital One · Derby

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 combines technical direction-setting, stakeholder collaboration and mentoring, taking ideas from prototype through to production in a hybrid Nottingham-based team.

Capital One · Leicester

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, mentor junior team members and drive prototypes through to production in collaboration with business stakeholders.

Harnham - Data and Analytics Recruitment · London · £100,000 – £120,000 pa

Sits within a private equity firm's value creation function, designing and building scalable data platforms and architecture for portfolio companies. Works hands-on with SQL and Python to establish data engineering foundations for analytics and AI-ready environments, while advising C-suite stakeholders across varied data maturity levels and technology stacks.

Harnham - Data and Analytics Recruitment · London · £55,000 – £65,000 pa

Mid-level data scientist role focused on fraud detection and mitigation, working across the full data science lifecycle. Responsibilities include processing large datasets, building and maintaining fraud detection, identity resolution and predictive machine learning models, evaluating new data sources, and monitoring model performance. Requires Python, SQL and production deployment experience, collaborating with data, technology, risk and product teams.

eFinancialCareers · Belfast

Hands-on engineering lead building AI-powered products and enterprise-scale applications, staying close to code through architecture, system design and code reviews. Sets technical direction across full-stack ecosystems (frontend, backend, APIs, cloud) using Python, Go, TypeScript, JavaScript or React, Kubernetes and microservices, while mentoring engineers and driving GenAI adoption.

eFinancialCareers · London

Architects, deploys and supports machine learning and GenAI infrastructure on AWS, building CI/CD pipelines and turning data science models into production systems. Works within an AI Enablement team using Terraform, Jenkins, Kubernetes and SageMaker, with a focus on auditability, versioning and data security while liaising with clients on requirements.

Isomorphic Labs · Cambridge, United States

Lead the safety assessment strategy for drug discovery projects from inception to regulatory submission, designing screening cascades and mechanistic experiments for small molecules and biologics. Partner with machine learning researchers to build predictive toxicology models and manage CRO relationships for outsourced wet-lab data generation, while mentoring junior scientists.

Isomorphic Labs · London

Drive DMPK strategy across drug discovery projects, designing screening cascades and mechanistic experiments with an AI-first approach. Integrate DMPK, PD and toxicology data to predict human dose and safety windows, and author regulatory documents. Work closely with AI/ML drug design platform teams in a hybrid London setting.

PhysicsX · San Francisco, United States · US$150,000 – US$190,000 pa

Builds and deploys machine learning pipelines for industrial physics applications, working directly with customers on-site to embed AI models into practical tools. Involves manipulating 3D point-cloud and mesh data, cloud and HPC infrastructure, and translating R&D outputs into reusable libraries and products.

PhysicsX · New York, United States · US$150,000 – US$190,000 pa

Delivery-focused role embedding physics-aware AI models into customer tools, working on-site with clients across aerospace, automotive and energy. Involves building reliable ML data pipelines, manipulating 3D point-cloud and mesh data, and translating R&D outputs into reusable libraries and products. Expects travel of 3-4 weeks per quarter to customer sites worldwide.

Entrust · Lisbon, Portugal

Builds and operates production ML systems for document classification and extraction, using VLM/LLMs and traditional ML to power identity verification for millions of users. Leads technical design from RFC to deployment, optimises GPU inference, and designs training, evaluation and labelling pipelines. Works in a squad-based team alongside Applied Science, Product and Data Science, mentoring engineers and driving reliability and scalability.