Artificial Intelligence Jobs

NVIDIA · Belfast

Lead security research, architecture and implementation for next-generation NVIDIA Networking products, taking a hands-on role defining hardware and software security solutions. Work across internal teams and external partners on IO interfaces, boot architecture, RISC-V, root-of-trust and secure platforms, guiding microarchitectural definition and open-source collaboration.

NVIDIA · Cambridge

Design and optimize compiler technologies for NVIDIA's CPUs, focusing on hardware-software co-design to enhance performance for AI and high-performance computing workloads. Collaborate with distributed teams and open-source communities to develop advanced compiler features and contribute to next-generation compute platforms. Analyze real-world workloads to drive improvements in both compiler toolchains and future CPU architectures.

CrowdStrike · London

Builds and owns major components of the Falcon endpoint sensor, a Windows kernel-mode driver that observes system activity, detects malicious behaviour and provides on-box prevention plus telemetry to the cloud. Work spans OS internals such as file system, memory, process and networking, with cross-platform components shared across Windows, Mac and Linux.

CrowdStrike · Ireland

Owns major components of the Windows kernel-mode sensor, architecting and building next-generation anti-virus and EDR security software. Works across core OS subsystems like file system, memory, process and networking, with cross-platform deliverables for Windows, Mac and Linux. Requires deep C++ and Windows kernel expertise, debugging memory corruptions and system performance, and leading projects in a distributed team.

Faculty · London

Lead the design, build and deployment of production-grade ML systems for national security and AI safety clients, including capability testing of frontier models. Act as a technical advisor to customers, define best practices for deploying ML at scale, and mentor junior engineers. Distinct for its focus on red teaming, evaluation and high-stakes government and frontier lab work.

Faculty · London

Scoping, building and delivering bespoke AI solutions for pharmaceutical and life sciences clients, managing individual work streams and translating operational requirements into user stories for engineering teams. Involves open-source analysis, workflow design and close collaboration with data science and ML experts, with a focus on high-stakes, user-centric decision-making.

Medtronic · Edinburgh · £76,800 – £115,200 pa

Designs and manages complex research data pipelines spanning clinical study data collection through validation trials, supporting an AI and algorithm R&D team. Owns data architecture, storage, security and ETL/ELT workflows across sensor platforms and HPC environments, working with Python, SQL and CI/CD automation to enable machine learning and signal processing applications.

University of Exeter · Exeter · £21,805 pa

Develop next-generation machine learning models to predict genetic interactions from large-scale perturb-seq data, evaluating graph neural networks and autoencoders for genome-wide scalability. The purely computational project feeds a lab-in-the-loop cycle with pancreatic cancer screens from the AstraZeneca and Cancer Research Horizons Functional Genomics Centre, with results released as open-source software.

Relation Therapeutics · London

Builds and scales generative and predictive models of cellular behaviour, implementing and optimising large neural networks and distributed training infrastructure. Acts as a bridge between ML research and production, profiling GPU workloads and optimising inference for use by data scientists and biologists on multi-modal biological data.

Harnham - Data and Analytics Recruitment · London · £75,000 – £85,000 pa

Owns the infrastructure, deployment and operational excellence of production machine learning systems within a growing AI and data organisation serving the public sector. Builds ML orchestration pipelines with Dagster, Airflow or Prefect, runs Kubernetes and Terraform/Bicep infrastructure, and maintains CI/CD and observability for models and LLM-powered solutions, working closely with data scientists and client-facing teams.

Tenth Revolution Group · London · £75,000 – £105,000 pa

Builds and deploys production LLM and Generative AI applications for enterprise clients, including RAG solutions, scalable data pipelines and AI-powered services. Works in a collaborative consultancy team of engineers and architects, applying software engineering best practices to take AI from proof of concept into production, with monitoring and optimisation of live systems.

Darktrace · Cambridge

Conducts independent and collaborative research across large language models, statistical methods and classical machine learning, prototyping ideas and implementing models into the wider software stack. Produces optimised solutions for latency and memory, including deployment on edge devices, working closely with software engineers and security analysts.

Harnham - Data and Analytics Recruitment · London · £80,000 pa

Builds internal AI agents, tools and prototypes within a small central technical team, turning complex group data into usable products at speed. Work spans Python, TypeScript and SQL, modernising legacy systems, wrapping systems in scalable APIs and contributing to shared data and entity platforms, with weekly demonstrable output.

Ocado · Sofia, Bulgaria

Maintains and improves a live customer-facing slot-availability application while writing Terraform/OpenTofu infrastructure as code for cloud environments. Extends backend services, APIs and data pipelines, and modernises the platform by tuning Airflow/Composer and reducing technical debt. Works in a small distributed team with monitoring, alerting and incident response duties.

eFinancialCareers · London

Design and build AI-powered tools, automations and integrations using Python, PowerShell and APIs, exploring Agentic AI and LLM use cases across internal business functions. Advise on embedding AI into enterprise architecture within a regulated investment management environment, working with engineering, product and business teams. Distinct for its focus on engineering productivity and architecture strategy rather than traditional ML modelling.