About the job
We’re building an ecosystem of digital products and services that power everyday life across the Middle East—fast, scalable, and deeply customer-centric. Our mission is to deliver to every door every day. We want to redefine what technology can do in this region, and we’re looking for a Machine Learning Engineer who can help us move even faster.
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What you'll do:
Team the company has some of the fastest, smartest, and hardest-working people we've encountered. As a Machine Learning Engineer (MLE1), Ads, you will be a core individual contributor responsible for the hands-on design, development, and deployment of production-grade Machine Learning models across our Ads division. 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 technical; you will partner with Product and Engineering teams across the company Ads to turn complex data into competitive advantages.
- Deeply engage in the technical work: building, training, and deploying production-grade models across our domains
- Contribute to solving "hard" problems involving relevance, ranking, bidding optimization, and fraud detection
- Partner with the broader engineering team to implement optimal deployment strategies for heavy ML workloads
- Adhere to and contribute to the standardization of ML Ops and best practices within the team
What you'll need:
- Experience: hands-on experience or relevant internships in Machine Learning or data science is preferred
- Foundational Knowledge: Strong academic or practical grounding in core machine learning concepts, statistics, and data structures
- Coding & Frameworks: Proficiency in Python and familiarity with standard ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn) and SQL
- Growth & Collaboration: Eagerness to learn from senior engineers, collaborate across product and engineering teams, and rapidly ramp up on MLOps best practices
- Tech Stack: Basic exposure to cloud platforms (like GCP) and data processing pipelines.
