Engineering Manager (ML)

Job Description

REQUIREMENTS

• 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

RESPONSIBILITES

• 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

Are you interested in this position?

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