Applied AI Manager – Talabat

This Applied AI Manager role sits within Talabat, the leading on-demand food and q-commerce app in the region since launching in Kuwait in 2004. The role is about applying AI practically to problems that actually move the business – things like demand forecasting, delivery time estimation, search and recommendation, or operational efficiency across a platform that handles enormous volumes of everyday deliveries.

Day to day, expect leading applied AI projects from problem definition through to production deployment, working closely with data science, engineering and product teams to make sure AI solutions actually solve the business problem they were built for, and managing the practical trade-offs between model sophistication, latency and reliability that matter enormously at Talabat’s scale. Applied AI work here is judged on whether it changes a real outcome – faster deliveries, better matching, lower operational cost – not just on model performance in isolation.

It suits someone with strong applied machine learning or AI experience who has shipped models into production before, is comfortable managing a team or cross-functional project, and understands that in a business like on-demand delivery, a model’s real-world robustness under messy, real operational data matters more than theoretical elegance.

Working on applied AI at Talabat means building solutions that run at genuinely large scale, across millions of everyday transactions throughout the region.

Applied AI at Talabat's scale directly touches the metrics that define the on-demand delivery business: how accurately delivery times are estimated, how efficiently orders are matched to riders, how relevant search and recommendations are to a hungry customer deciding what to order. Small improvements in these areas, multiplied across millions of everyday deliveries, translate into real operational savings and a meaningfully better customer experience - exactly the kind of compound effect that makes applied AI investment worthwhile at this scale. This Applied AI Manager role is accountable for making sure that investment actually lands in production rather than staying theoretical, which is often the harder and more valuable half of AI work. Because the role sits across data science, engineering and product, it also has influence over how well AI initiatives integrate with the rest of the platform rather than existing as isolated experiments. In a highly competitive q-commerce market, the pace and quality of applied AI deployment is a genuine differentiator between platforms competing for the same customers and riders.

Leading applied AI work at genuine platform scale - millions of daily transactions, real operational constraints, messy real-world data - builds a kind of practical AI leadership experience that's considerably rarer and more valuable than working on AI in a purely research or small-scale product context. The skill of shipping AI into production and making it actually move business metrics, rather than just building a model that performs well in a notebook, is exactly what the AI job market is increasingly looking for as more companies move from AI experimentation to AI deployment. Working across Talabat's data science, engineering and product functions also builds broad cross-functional leadership experience that pure technical roles don't always offer. Talabat's position as the region's leading on-demand delivery platform, and its backing by Delivery Hero, gives this kind of applied AI experience strong recognition across the regional and international tech industry. For someone building a long-term AI career, this role is a strong platform toward senior applied AI leadership, head of data science, or broader AI strategy roles, either within Talabat as it continues to invest in AI-driven operations or at other large-scale consumer tech and logistics platforms. Few roles offer this much hands-on exposure to AI operating under real commercial constraints rather than idealised conditions, which is exactly the experience the market is short of.

• Strong experience in applied machine learning or AI, with a track record of shipping models into production
• Experience leading or managing AI/ML projects, ideally with some people or project leadership responsibility
• Comfort working cross-functionally with data science, engineering and product teams
• Understanding of the practical trade-offs between model performance, latency and reliability at scale
• Experience with real-world, messy operational data rather than purely clean academic datasets
• Strong communication skills to translate AI capability into business impact for non-technical stakeholders