Top Tech Jobs & Startup Jobs in London

Lead Preply’s global IT function, setting strategy while remaining hands-on with identity, endpoint management, automation, and scripting. Manage international device lifecycle, workplace technology, SaaS spend, vendor relationships, IT security controls, dashboards, and compliance readiness. Develop a high-performing team, introduce AI-native operations, partner with Security and Finance, and advise C-level stakeholders on technology, cost, risk, and employee experience.

Own the strategy, roadmap, and launch of Preply’s new non-language-subjects vertical. Lead a 0-to-1 product initiative spanning tutor and learner experiences, partnering with design, research, data science, engineering, marketing, and supply teams. Prioritize opportunities, validate decisions through experiments and research, define success metrics, and influence leadership while scaling a new marketplace business.

Own the strategy, roadmap, OKRs, and adoption of internal platform capabilities that improve developer productivity, reliability, delivery speed, and cost efficiency. Identify engineering pain points, define product requirements, evaluate technical trade-offs, establish standards, and lead migrations and deprecations. Partner closely with engineering managers and senior engineers while using developer experience research, incident reviews, DORA metrics, SLOs, and platform data to guide decisions.

Lead the development of AI-powered personalized learning solutions, including deep learning, NLP, information retrieval, LLM, and multimodal systems. Shape Preply’s scientific roadmap, advise senior leadership, partner with product and engineering teams, mentor scientists and engineers, and establish machine learning architecture, research, and deployment best practices. The role spans research through production and focuses on scalable solutions for personalized learning and intelligent tutor systems.

Own data strategy and experimentation for a critical product domain, influencing senior leadership decisions through causal inference, forecasting, LTV modeling, retention analysis, pricing analysis, and scenario planning. Define key metrics, lead strategic analytical initiatives, improve measurement frameworks, and mentor senior data scientists while partnering with Product, Engineering, and Business leaders.

Own and scale Preply’s revenue operations systems, architecture, automation, data governance, reporting, and tooling strategy. Lead Salesforce and connected GTM platforms, improve lead-to-renewal workflows, build dashboards, manage vendors and budgets, and deliver insights for forecasting and growth decisions. Lead a small team while partnering across Sales, Marketing, Customer Success, Finance, Product, and Enablement to implement scalable processes and improve adoption, accuracy, and efficiency.

Lead technical strategy and architecture for Learning Teams, drive end-to-end strategic initiatives, evolve core services for scalability and reliability, improve engineering practices and developer experience, mentor senior engineers, and make build-vs-buy and tooling decisions.

Lead design and evolution of core analytical data models, define analytics engineering standards, build and optimize ETL/ELT pipelines, own the BI/semantic layer (Looker/LookML), drive data quality and governance, partner with cross-functional teams, and mentor analytics engineering across the company to enable reliable, scalable decision-making and experimentation.

Leads and develops high-performing engineering teams responsible for learner growth or monetization products. Sets technical and product strategy, drives execution, coaches engineers, guides architecture and technical trade-offs, and promotes engineering best practices, reliability, quality, and continuous improvement. Partners with Product, Data, and Design to prioritize high-impact initiatives and deliver measurable customer and business outcomes.

Lead design and roadmap for a scalable ML/GenAI platform: lifecycle architecture (training, deployment, monitoring), cloud-native GPU infrastructure, CI/CD for ML, observability and governance, LLM/vector retrieval capabilities, self-service tooling, cross-functional alignment, and mentoring senior engineers to accelerate safe, cost-efficient production ML at company scale.