Top Tech Jobs & Startup Jobs in London

Own AI stockbroking and other financial product areas from user problem definition through launch and iteration. Define product direction, scope, metrics, roadmaps, and launch plans while partnering with engineering, design, data, operations, compliance, and business teams. Simplify complex financial workflows, use research and performance data to guide decisions, manage trade-offs, and drive continuous improvement with high ownership.

Own AI Investing product areas and define user problems, scope, metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify financial products, ship features, monitor performance, and iterate using data, research, feedback, and production insights. Balance speed, user experience, business impact, compliance, and operational complexity while driving execution with high ownership.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.

Lead the architecture and hands-on development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design orchestration, state management, tool use, retries, fallbacks, and failure handling for long-running workflows. Build production APIs, improve observability and reliability, make trade-offs involving latency, cost, security, and user experience, and lead a small senior engineering team through technical reviews, mentorship, and execution.

Own company-wide performance management and OKR processes, analyze people and operational data, build organizational performance dashboards, define HR systems requirements, and improve workflows. Partner with leaders on performance reviews, calibration, promotions, career development, accountability, culture, and strategic people initiatives. The role focuses on organizational effectiveness and scalable people operations in a fast-growing environment.

Lead application engineering for an AI email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, guide a senior engineering team, make architecture and reliability trade-offs, and establish observability, monitoring, testing, and operational excellence. Partner with ML leadership to ship scalable, maintainable, and high-performing AI-powered experiences.

Lead the engineering strategy and execution for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, lead a senior engineering team, make architecture and scalability decisions, and establish strong observability, monitoring, testing, security, and operational foundations. Partner with ML leadership to deliver fast, reliable, maintainable product experiences.

Lead end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

Own the end-to-end product direction and quality of AI-powered systems. Define requirements, evaluation frameworks, quality bars, and feedback loops while balancing model quality, latency, cost, reliability, safety, and user experience. Partner deeply with ML, backend, and client engineers on system design and iteration, identify failure modes, and drive execution in a fast-moving zero-to-one environment.