Applied AI Manager – Talabat

Talabat, the leading on-demand food and q-commerce app across the region since launching in Kuwait in 2004, is hiring an Applied AI Manager for its Dubai team — a role focused on turning AI and machine learning capability into actual product and operational impact, not just research. This means identifying where AI can genuinely move the needle across Talabat’s business — delivery logistics, personalisation, demand forecasting, customer experience — and owning the path from idea to deployed, measurable outcome.

The role sits at the intersection of technical depth and product pragmatism: managing or closely partnering with data scientists and engineers, translating business problems into AI-solvable ones, and making the judgment calls about where machine learning adds real value versus where a simpler solution would do the job better. At a business operating at Talabat’s scale and transaction volume, even modest AI-driven improvements in areas like delivery routing or demand prediction can compound into meaningful operational gains.

This suits someone with hands-on AI/ML experience who’s equally comfortable managing a technical roadmap and explaining its business value to non-technical stakeholders, in a fast-moving, consumer-facing tech environment.

Talabat operates at a transaction volume where small efficiency gains compound fast — a modest improvement in delivery routing, demand forecasting, or personalisation, applied across millions of orders, translates into real operational savings and a better customer experience at scale. This role exists to find and ship exactly those gains, which makes it one of the more direct levers on Talabat's unit economics and customer satisfaction available in the business. Applied AI done well here doesn't just produce an interesting model — it produces measurable outcomes: faster deliveries, better demand prediction, more relevant recommendations, each of which affects real customer retention and operational cost at a company handling the kind of order volume Talabat processes daily across the region. At the volumes this business processes, the compounding effect of applied AI work is real — small, well-targeted improvements add up across millions of transactions in a way that's hard to replicate through manual process improvement alone, which is exactly why this kind of role carries outsized influence relative to its headcount.

Applied AI roles that sit this close to real product and operational impact — rather than purely research-focused ML roles — are some of the most sought-after positions in tech right now, and doing this work at a company with Talabat's scale and order volume provides genuinely rare, large-dataset experience that's hard to replicate at a smaller company. The combination of technical AI/ML depth with product and business fluency built in this role is exactly the profile that top tech companies and well-funded startups are actively recruiting for. Being part of the Delivery Hero group, which operates across dozens of markets globally, also opens longer-term mobility within a genuinely large, international tech organisation — regional AI leadership roles, or cross-market applied AI positions, become realistic next steps for someone who performs well here. For an AI/ML professional building a long-term career in applied, product-connected machine learning, few roles in the region offer this density of real-world impact and scale. It's also worth noting how quickly applied AI experience like this compounds in market value — the skill set is in genuine, growing demand across the region, and hands-on production experience at real scale is still relatively rare, which keeps someone who's built it here in a strong position for what comes next.

• Strong hands-on experience in applied machine learning or AI, with a track record of shipping models into production
• Ability to translate business problems into AI-solvable technical problems and back again
• Experience managing or closely collaborating with data science and engineering teams
• Good judgment on where AI/ML adds genuine value versus simpler, more pragmatic solutions
• Familiarity with on-demand delivery, logistics, or consumer tech business models is a strong plus
• Clear communication skills for presenting technical work to non-technical stakeholders