Engineering Manager (ML)

Own the end-to-end product data pipeline: ingestion from feeds and plugins, ML enrichment, moderation and publication, with clear SLAs for freshness, coverage and quality Lead the ML roadmap for catalogue intelligence: category tree and attribute coverage, translation quality, ML-assisted moderation, item embeddings and recommendations Lead large cross-team projects and drive them to production Contribute to quarterly planning and roadmap definition; define and report OKRs for catalogue quality and personalisation Review feature designs and ensure non-functional requirements are met, including ML evaluation, inference cost, latency and data residency Build and maintain the evaluation and labeling infrastructure that lets the team measure every model change before it reaches production Oversee technical debt management and incident handling across ML and backend services Hire, evaluate, and motivate team members; grow ML engineers into owners of business outcomes Build cross-team and cross-functional collaboration with Shopping, Offers, Monetisation, catalogue operations and partner support to increase efficiency Foster a results- and business-oriented culture Monitor key team performance indicators Ensure process and delivery transparency for stakeholders and partner functions Optimise processes to improve productivity Desired Candidate Profile 6+ years of engineering experience, including 3+ years building production ML systems (NLP, LLM applications, embeddings, or classification at scale) 2+ years as an Engineering Manager or ML Team Lead at a fast-growing e-commerce, marketplace or fintech company Hands-on experience shipping LLM-based products: prompt and pipeline design, fine-tuning, evaluation, cost and latency control, self-hosted and API-based models Experience building and operating large-scale data and ML pipelines (batch and streaming), and making them observable, reproducible and reliable Solid backend fundamentals; you are comfortable reviewing Go and Python services and reasoning about distributed systems Our stack: Python, Go, PostgreSQL, Pub/Sub, BigQuery, GCS, Kubernetes, Google Cloud Platform, Airflow, and a microservices architecture A strong grasp of ML evaluation: golden datasets, labeling workflows, offline metrics, and A/B testing tied to business outcomes Product sense: you connect catalogue quality to conversion, discovery and merchant growth, and you can prioritise accordingly A proactive mindset and the ability to work independently Strong communication skills in English (B2 level or higher)

Nice to Have

Experience with product catalogues, PIM systems, or marketplace content moderation Experience with Arabic-language content Familiarity with data residency and regulated-data requirements