Top Tech Jobs & Startup Jobs in London

Build and deploy production-grade machine learning software, tools, and infrastructure for high-stakes AI safety and national security applications. The role involves creating scalable ML solutions, evaluating frontier AI models, making architectural decisions, advising clients, and collaborating with engineers, data scientists, commercial teams, and research labs. Candidates should have strong Python and software engineering skills, cloud and containerization experience, ML lifecycle knowledge, and familiarity with LLM applications and AI safety evaluation.

Lead the architecture, development, deployment, and scaling of secure, production-grade machine learning systems and AI safety evaluations. The role includes technical scoping, cloud infrastructure design, client advising, cross-functional collaboration, engineering standards, and mentoring. Candidates need strong Python and software engineering expertise, experience with LLM applications, TensorFlow or PyTorch, cloud platforms, cybersecurity, Docker, Kubernetes, and full ML lifecycle operations.

Build and scale AI-powered systems in collaboration with data scientists and product managers. Develop reliable frontend and backend architectures, data pipelines, and cloud infrastructure. Deploy machine learning outputs into production-ready applications, provide technical expertise to customers, and manage the full software lifecycle while balancing rapid prototyping with long-term maintainability.

Owns end-to-end recruitment for commercial and central-function roles, from scoping and sourcing through interviews, offers, and onboarding. Advises hiring managers, builds diverse passive-talent pipelines, designs structured assessments, analyzes recruitment metrics, supports inclusion initiatives, and represents the employer brand at industry events. The role contributes to hiring strategy and delivers a high-quality, inclusive candidate experience in a fast-growing AI organization.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable reusable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture, ML deployment standards, and customer advisory activities while translating technical concepts for non-technical stakeholders.

Lead delivery of AI safety evaluations, frontier model red teaming, and safeguard testing projects. Build trusted relationships with clients, data scientists, and technical partners; translate AI safety research into strategic solutions; develop proposals to grow AI safety and governance work; advise clients on implementation; and mentor team members across a high-impact portfolio.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture decisions, and ML deployment standards while advising technical and non-technical stakeholders on production AI systems.

Build and scale AI-powered systems in cross-functional teams. Responsibilities include developing scalable frontend and backend architectures, deploying machine learning outputs into production, building data pipelines and cloud infrastructure, and advising customers on complex technical requirements. The role requires ownership across the application lifecycle, balancing rapid prototyping with maintainability, and collaborating with data scientists and product managers.

Supports the delivery of bespoke AI transformation projects for retail and consumer clients. Responsibilities include managing workstreams, gathering stakeholder and user needs, conducting open-source analysis, translating operational requirements into user stories, designing and testing workflows, collaborating with engineering teams, and contributing to strategy and internal capability development.

Lead the design, development, and deployment of scalable, production-grade machine learning systems for diverse clients. Define ML deployment standards, make architectural decisions, collaborate with technical and commercial teams, advise customers, and mentor junior engineers. The role requires strong Python and software engineering expertise, experience operationalizing TensorFlow or PyTorch models, cloud architecture, security, infrastructure, Docker, and Kubernetes.