Artificial Intelligence Jobs

Isomorphic Labs · London

Lead and contribute to core machine learning research applying large language models to drug discovery, developing and refining LLM-driven approaches across biology and chemistry problems. Work spans post-training, reasoning, test-time scaling, tool-use, alignment, agents, reinforcement learning and fine-tuning, with model training and inference engineering at scale. Interdisciplinary collaboration with research scientists and engineers, plus technical mentorship and project leadership depending on experience.

Isomorphic Labs · London

Lead the hardening and scaling of an inference platform serving fleets of cutting-edge ML models for scientific applications. Set strategy and roadmaps, architect next-generation services to solve scaling limits, and partner with research, product and operations teams to align infrastructure with biotech R&D cycles.

Synthesia · London

This role focuses on translating Synthesia’s brand into global experiential formats, including trade shows, conferences, customer events, and digital experiences. The designer will lead end-to-end creation of spatial environments, signage, print, merchandise, and digital content, ensuring cohesive brand expression across physical and virtual touchpoints. Emphasis is placed on production quality, cross-functional collaboration, and scalable systems for global execution.

Synthesia · UK Remote

Shape how the company shows up in experiential moments worldwide, designing booths, spatial environments, signage, print and merchandise from concept to production. Develop cohesive event identities across physical and digital touchpoints, storyboard motion content for keynotes, and art-direct external vendors. Works in a small senior brand design team with low process and high ownership, requiring travel across Europe and the US.

PhysicsX · New York, United States · US$200,000 – US$250,000 pa

Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimization) into customer production environments, building scalable training and inference pipelines and APIs on cloud and on-prem. Leads scoping and architecture design, mentors engineers and data scientists, and travels to customer sites roughly 3-4 weeks per quarter to embed AI models on-site.

PhysicsX · London

Builds and validates high-fidelity multiphase CFD and coupled CFD-DEM models, covering geometry clean-up, meshing, simulation, post-processing and experimental validation for granular and particle-laden systems. Automates parametric CAD and simulation pipelines for design optimisation and DoE, generating datasets for ML surrogate models on cloud and HPC resources, while working on-site with customers and mentoring junior colleagues.

Luminance · London

Works across Sales, Product and Customer Success to help enterprise customers integrate Luminance's AI platform, building hands-on integrations with systems such as Salesforce, ServiceNow and Slack. Acts as the technical expert supporting Account Executives, managing integration issues and creating reusable toolkits, APIs, SDKs and customer-facing documentation.

Luminance · Cambridge

Lead complex engineering projects across the full SDLC, mentor other application engineers, and collaborate with cross-functional teams to define technical requirements and system architecture. The role also involves driving best practices in coding, testing and maintenance, and becoming a subject matter expert in one or more areas of the technology stack.

Faculty · London

Lead high-impact AI projects for retail and consumer clients, defining data science approaches, designing software architectures, and mentoring colleagues. Own the technical side of client relationships, scope projects, and embed responsible AI practices while delivering bespoke algorithms and production-grade systems.

Faculty · London

Lead delivery across an AI safety portfolio, overseeing frontier model evaluations and red teaming projects for government and public sector clients. Act as the primary link between customers and data scientists, translating safety research into strategy while growing accounts and mentoring team members.

VIQU IT · London · £75,000 – £80,000 pa

Designs and deploys AI/ML models for forecasting, propensity, churn and customer segmentation, owning end-to-end workflows from feature creation to ML Ops deployment and monitoring. Works hands-on across multiple products, architecting bespoke datasets and coaching junior data scientists in a cloud-native, AI-first SaaS team.

Anthropic · London · £255,000 – £325,000 pa

Product engineer building internal tools and platforms, working across backend, frontend and infrastructure with Python, TypeScript and React. The role sits at the intersection of engineering, product and design, requiring direct user consultation, spotting patterns across requests and turning them into reusable primitives and abstractions rather than one-off features.

Anthropic · London · £325,000 – £485,000 pa

This role involves owning and scaling Kubernetes control plane components to support Anthropic's large-scale AI training infrastructure. You'll extend the scheduler for topology-aware and gang scheduling of ML workloads, operate core cluster services like service discovery, and ensure high availability across multi-cloud accelerator fleets. The work is deeply technical, focusing on reliability and performance at extreme scale in collaboration with research and inference teams.

Wayve · London

Works at the boundary between onboard software and vehicle hardware, bringing up new vehicle platforms on benches, HiL systems and vehicles, including at international test locations. Builds and maintains vehicle-facing components and shared frameworks in C++, Python or Rust on Linux-based robotic and automotive systems using ROS2, CAN and Automotive Ethernet, with diagnostics, tooling and documentation for long-term supportability.

Wayve · London

Leads complex, multi-team programmes across Level 2 and Level 4 autonomous driving products, translating product milestones into integrated cross-team schedules and delivery plans. Reviews technical progress, backlog health and system-level evidence to assess milestone readiness, managing dependencies and critical-path activities across engineering, product, safety and validation. Improves lifecycle-planning practices and tooling to surface risk earlier and deliver more predictably.