Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate directly with customers and technical teams to prototype, deploy, and iterate on high-impact tools across aerospace, energy, and advanced manufacturing. Work across backend services, APIs, and cloud infrastructure while contributing to core platform development.
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate with data scientists and customers to develop, prototype, and deploy production-grade tools using Python, FastAPI, and cloud infrastructure. Work across industries such as aerospace, energy, and semiconductors, with opportunities for international travel to support on-site customer deployments and iterate on solutions based on direct user feedback.
This role involves working with cutting-edge AI and cybersecurity technologies to design and develop cyber ranges, CTF challenges, and evaluation metrics. You will collaborate with a team of experts to ensure the UK government and its partners have an accurate view of AI risks and capabilities.
The Engineering Lead will manage a team of engineers focused on developing evaluations, data pipelines, and complex multi-agent setups to study human-AI interactions. The role involves leading ambitious research projects, designing scalable system architectures, and building automated pipelines for human-AI experiments.
This internship involves hands-on work on real projects, including building reinforcement learning pipelines, developing evaluation infrastructure, and creating fraud-detection systems. Interns will work closely with experienced engineers and contribute to impactful AI applications.
Lead the technical direction of AI and machine learning projects, designing production-grade systems and guiding engineering teams across the full project lifecycle. Focus on delivering robust, scalable solutions for clients in energy, sustainability, and infrastructure sectors. Drive innovation through reusable tools, architectural leadership, and mentoring across multidisciplinary teams.
This role involves leading backend and edge/IoT engineering efforts to build production-grade AI systems for high-stakes defence applications. The engineer will bridge machine learning research with robust software delivery, using Python and compiled languages like Rust or C++, while implementing scalable CI/CD, containerisation, and Kubernetes deployments. Collaboration with data scientists and mentoring of engineering peers are key aspects of the position.
Design and develop core data storage, streaming, caching, and indexing platforms to support large-scale AI and LLM applications. Work closely with cross-functional teams to build reliable, scalable systems that underpin AI data engines used by major enterprises and government agencies. Focus on distributed systems, cloud infrastructure, and data platform reliability in a high-impact AI environment.
Design and manage cloud infrastructure for a cutting-edge AI-driven drug discovery platform, supporting research and production workloads on GCP. Work closely with scientists, ML researchers, and engineers to build scalable, secure, and automated systems using Kubernetes, Terraform, and modern DevOps practices. Operate in a hybrid environment with a focus on innovation and scientific impact.
This role involves building and maintaining large-scale data infrastructure to support cutting-edge AI models, with a focus on data pipelines, curation strategies, and tooling for training high-quality models. The engineer will work on data processing, filtering, and transformation systems, as well as develop classifiers and labeling models to improve data quality. The position emphasizes autonomous problem-solving and close collaboration with research teams in a fast-paced, AI-first environment.
The IT Engineer will play a key technical role in scaling ElevenLabs' global IT infrastructure during a period of rapid growth. Responsibilities include managing identity systems, endpoint security, office networks, and compliance, while automating IT processes across a distributed environment. The role works closely with Security and Operations to ensure secure, reliable, and productive systems for a globally remote workforce.
Lead and scale a presales engineering team in London, shaping the technical sales approach for a leading AI video platform. You'll hire and mentor Solutions Engineers, refine demo methodologies across Studio, Roleplay, and Interactive Avatars, and directly influence high-impact deals through discovery, PoCs, and executive storytelling. The role involves close collaboration with Sales, Product, and Customer Success to drive revenue and product feedback, while establishing scalable processes in a high-growth AI company.
Lead and scale a security engineering team at the forefront of AI-driven drug discovery, designing and implementing a robust, AI-first security architecture. Translate complex scientific and business objectives into secure, automated systems while enabling high-velocity research in a regulated, cloud-native environment. Collaborate closely with ML researchers, medical teams, and Google/Alphabet partners to secure cutting-edge digital biology platforms.
Lead the engineering team behind Multiverse's Diagnose & Prescribe (D&P) system, which assesses skill gaps and guides learners and employers to the right apprenticeships. You'll own technical direction, team health, and delivery across AI-driven services including RAG, LLM-scored assessments, eligibility logic, and onboarding flows. Work closely with Product, Design, and GTM to shape an AI-native platform while evolving services out of a legacy monolith.
This role involves developing advanced multimodal generative models that enable natural, real-time conversational interactions by integrating text, voice, and video. The engineer will lead research and implementation of low-latency, emotionally expressive AI systems, working across pretraining, post-training, and production deployment. Key focus areas include neural codecs, diffusion models, and streaming architectures to enhance realism and interactivity in Synthesia's AI video platform.
