Top Tech Jobs & Startup Jobs in London

Lead the creative direction and design strategy for Topshop’s tailoring category. Research trends, develop seasonal concepts, create commercially successful tailoring ranges, and produce design packs, CADs, and technical briefs. Collaborate with buying, technical, product development, suppliers, and supply chain teams on fabrics, fit, quality, cost, and delivery. Analyze performance and customer feedback, present concepts to stakeholders, and champion innovation across tailored separates and wardrobe essentials.

Supports day-to-day trading and optimization of ASOS websites across devices and markets. Responsibilities include site merchandising, search optimization, category and navigation management, trade reporting, customer journey analysis, homepage and promotional coordination, CRM processes, campaign launches, and digital product rollouts. The role works cross-functionally to improve onsite customer experience, conversion, and commercial performance while maintaining website standards.

Supports the optimisation and delivery of ASOS Customer Care digital products, tools, and journeys. Responsibilities include translating customer and operational needs into product changes, monitoring performance, gathering frontline insight, supporting experimentation, documenting journey flows, contributing to roadmap prioritisation, and helping with rollout, training, and adoption. The role collaborates with Product, Engineering, Technology, and Operations teams to improve efficiency, automation, customer satisfaction, and advisor experiences.

Lead the vision, strategy, and roadmap for ASOS’s enterprise data ecosystem and platform foundations. Define data product requirements, prioritize investments, collaborate with engineering and business stakeholders, and measure product outcomes. Establish data governance, quality, documentation, security, compliance, and ownership standards while enabling analytics, AI, and data-driven decision-making. Champion customer-focused discovery, iterative delivery, innovation, and organizational change across cross-functional Agile teams.

Designs and delivers scalable data solutions on Azure, collaborates with engineers and architects, supports data and telemetry understanding, performs proof-of-concept work, and builds reliable, tested, maintainable systems using CI/CD. The role contributes to agile delivery, engineering standards, data modelling, performance and cost optimization, while developing expertise in modern data technologies.

Develop and deploy scalable machine learning models and intelligent systems for ASOS. Responsibilities include applying statistical, optimization, and ML techniques; designing experiments; evaluating model and business impact; partnering with engineers to productionize solutions; collaborating with product and business stakeholders; and contributing to ML best practices, research, code quality, and technical direction.

Leads the design, development, evaluation, and deployment of scalable machine learning solutions for complex business challenges. Identifies ML opportunities, researches emerging methods, improves model performance and reliability, creates evaluation frameworks, and writes production-quality code. Partners with engineering, data, product, analytics, and business teams, provides technical leadership, mentors scientists, and communicates recommendations to diverse stakeholders.

Design, build, deploy, and operate large-scale batch and real-time machine learning systems for search, ranking, recommendations, and personalization. Collaborate with scientists, engineers, and product managers to deliver measurable customer impact. Improve production systems, engineering practices, and ML standards while mentoring engineers and contributing to technical direction.

Design, build, deploy, and operate large-scale machine learning systems for personalized outfit recommendations and fashion discovery. Partner with scientists and engineers to productionize deep learning models, optimize batch and real-time services, and improve reliability, observability, and scalability. Contribute to architecture decisions, shared ML capabilities, MLOps practices, and AI-powered product experiences. Mentor engineers and collaborate with product teams while exploring generative AI and LLM applications.

Build and maintain production machine learning systems for personalization, product discovery, recommendations, and ranking. Develop, deploy, monitor, evaluate, and optimize models in batch and real-time environments. Collaborate with applied scientists, software engineers, and product partners to productionize experiments, improve MLOps tooling, and support scalable machine learning platforms. Contribute to next-generation recommendation, sequence modeling, outfit generation, and AI-powered styling experiences.