AI Jobs in London

View detail

Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

View detail

The Staff Software Engineer is responsible for owning the end-to-end technical vision for the EDR telemetry and detection workstream, rallying the team from concept through shipping, iterating, and deprecating. This includes producing production code contributions in a modern backend language such as Go, Rust, or Python within a service-oriented environment and setting technical standards through design reviews, code quality, and operational discipline by example. The role involves mentoring engineers, building frameworks and architecture to enable high performance, partnering with the hiring team on recruiting and leveling engineers, and holding the team accountable for outcomes by managing risks and tradeoffs early and in writing. The engineer translates ambiguous product goals into concrete technical roadmaps, makes decisions regarding build versus buy or integration with business context, partners closely with product management in PRD reviews and sprint planning, and sequences MVP development effectively. Domain expertise is required in EDR platforms including telemetry, API level, detection logic, alert triage, and SOC team workflows. The engineer builds ground truth datasets, manages false positive and false negative tradeoffs and confidence scoring, and owns the detection and measurement methodology, including ground truth methodology, confidence scoring, calibration, and defining what constitutes correct tuning recommendations. The position requires collaboration, and contributing both to leadership and hands-on coding, and may include up to 10% travel.

View detail

Lead and grow a team of Forward Deployed Engineers delivering production systems with frontier models; own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality; codify effective practices into tools, playbooks, and roadmap inputs to create leverage for OpenAI and its developer community; notice and urgently raise early indicators related to product behavior, customer environments, or delivery practices; use judgment to determine necessary actions; set high performance standards for FDEs and support individual growth through direct, actionable feedback; define staffing and support structures for scalable field teams without added complexity.

View detail

The Computer Vision Engineer will deliver hands-on computer vision work and architect technical solutions for complex project requirements. They will lead the technical delivery of computer vision projects and provide expert guidance to multidisciplinary teams throughout the development lifecycle. The role includes contributing expert computer vision insight to bids and identifying opportunities to integrate advanced visual intelligence into customer solutions. The engineer will stay at the forefront of the field by mastering State-of-the-Art developments and sharing best practices across the business unit. They will represent the organization internally and externally as a subject matter expert in computer vision, partner with leadership to define the technical strategy for computer vision work, take ownership of capability development within the Defence domain, and mentor and develop team members interested in computer vision, fostering a continuous learning and technical excellence environment.

View detail

Responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating models, benchmarking quality, publishing models and datasets, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

View detail

Define and drive architecture for scalable, extensible Reinforcement Learning Environments (RLE) systems and data pipelines. Lead development of platform capabilities enabling rapid domain creation. Partner with Research, Product, and Operations to shape strategy and execution. Set standards for reliability, observability, performance, and data quality. Mentor engineers and elevate engineering excellence across the team. Identify and solve systemic bottlenecks in scaling environments and data generation.

View detail

You will own features end-to-end, holding a piece of work from contract through rollout across whichever surfaces it needs to touch, including the TypeScript/Node backend, the iOS app, the Android app, or all three. You will deliver the entire feature rather than handing off parts outside your domain. Early tasks include shipping meaningful production code on one of the products, defining and defending API contracts with mobile and design teams before implementation, and iterating on those contracts. You are expected to develop a point of view on the team's workflow and address inefficiencies, working across stack boundaries to deliver complete features and get them in front of real users.

View detail

Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their software development lifecycle, technical roadmaps, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

View detail

The AI Productivity Engineer will take clear ownership of rapid AI adoption across the engineering organization by building AI-powered tools and systems that improve engineering productivity, reducing friction, automating repetitive tasks, and embedding intelligence into workflows. Responsibilities include identifying high-friction areas in engineering workflows, designing and building production-grade AI-powered developer tooling for coding, testing, PR reviews, and debugging, building contextual AI assistants using internal data and tools, exploring, prototyping, and productionizing AI solutions, automating workflows across platforms like GitLab, Jira, CI/CD, Slack, and observability tools, designing and operating internal AI services and orchestration layers, owning solutions end-to-end from discovery to iteration, working hands-on with engineering teams to remove friction and enable tool usage, and measuring success through adoption, impact, and tangible time saved for engineers. The role explicitly excludes building AI features for customer-facing products, speculative AI research without clear outcomes, acting as general internal support, and owning generic ML infrastructure unrelated to developer productivity.

View detail

As the Defense / Edge Tech Lead, you will own the technical direction for deploying Deepgram's speech-to-text (STT) and text-to-speech (TTS) models to edge and embedded environments. Your responsibilities include leading the technical strategy for edge deployment, defining the architecture for on-device, on-premises, and air-gapped inference across diverse hardware targets. You will optimize models for edge and embedded platforms through quantization, pruning, distillation, and runtime optimization to meet latency, memory, and power constraints. You will partner with hardware vendors like Qualcomm and Motorola for SDK integration, performance benchmarking, and joint go-to-market efforts. Supporting defense customer requirements through AWS NatSec partnerships by translating mission requirements into engineering deliverables is also part of your role. You will design and build edge runtime infrastructure such as model packaging, deployment pipelines, OTA update mechanisms, and telemetry for devices in low or no connectivity environments. Deployments must be hardened for security-sensitive environments with features like secure boot chains, encrypted model storage, tamper detection, and audit logging. You will benchmark and validate performance across hardware platforms, establishing test suites for latency, accuracy, power consumption, and resource utilization. Collaboration with Research and Engine teams to influence model architectures toward edge-friendly designs is expected. Furthermore, you provide technical leadership to cross-functional teams on defense and edge projects, set engineering standards, review designs, and mentor engineers on systems and optimization practices.