Who we are
noon, the region's leading consumer commerce platform. On December 12th, 2017, noon launched its consumer platform in Saudi Arabia and the UAE, expanding to Egypt in February 2019. The noon ecosystem of services now includes marketplaces for food delivery, quick-commerce, fintech, and fashion. noon is a work in progress; we're six years in, but only 5% done.
noon's mission: Ring every doorbell, everyday.
About the role
As Head of Applied ML, Search & Ads, you will build, direct, and lead from the trenches a team of ML engineers operating across our Search and Ads/Monetization divisions. You will be responsible for delivering high-impact models and deployments that power critical functions like ranking, bidding, and click-fraud detection.
This role is deeply embedded within the business; you will work as a strategic partner to the Product and Engineering leadership of noon Search and noon Ads to turn complex data into competitive advantages.
What you'll do:
Lead from the trenches
- Deeply engage in the technical work: building, training, and deploying production-grade models across our domains.
- Solve hard problems involving relevance, ranking, bidding optimization, and fraud detection.
- Partner with the broader engineering team to define and implement optimal deployment strategies for heavy ML workloads.
Build the team
- Recruit and scale a team of experienced ML engineers to support our Search and Ads divisions.
- Hire, mentor, and accelerate the growth of fresh graduates joining the team.
- Structure the team for long-term success, with the goal of eventually allowing these units to operate with high autonomy within their respective divisions.
Direct ML Engineering
- As this is a relatively nascent practice at noon, you will set the strategic direction for how ML operates (e.g., standardizing ML Ops and best practices).
- While the immediate focus is on Applied ML, you will build the foundation that allows the team to expand into specialized research over time.
What you'll need:
- Experience: 7+ years in ML; experience in e-commerce, search, or ad-tech is highly preferred.
- Architectural Ownership: The ability to build and scale an ML Ops stack from the ground up.
- Production Focus: Experience working in high-scale, latency-sensitive environments.
- Domain Expertise: A strong understanding of ranking, forecasting, and candidate diversity.
- Tech Stack: Experience with cloud platforms (GCP / Vertex AI is a significant plus).
- Leadership Hybrid: We value candidates who have led teams before, but we are also open to Staff+ level Individual Contributors who are ready to maintain a high personal output while stepping into people management.
