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Tabby is a fintech platform that enables flexible payments (buy-now-pay-later) for over 20 million users across the GCC region and globally. The company partners with 65,000+ brands including Amazon, Noon, IKEA, and SHEIN, processing over $10 billion in annual transaction volume. Founded in 2019, Tabby has raised over $1 billion and is valued at $4.5 billion.
You will lead the Content Quality & Personalisation team within Tabby Marketplace, which owns the data infrastructure powering product discovery. The team manages a catalogue of 25M+ products from thousands of merchants, ingested through feeds and e-commerce plugins (Shopify, Salla, Zid, Amazon, etc.), then enriched, categorized, translated, moderated, and published—largely through ML automation.
As Engineering Manager, you will lead a cross-functional team of ML engineers, backend engineers, frontend engineers, QA specialists, and a product analyst. The team operates several critical systems: an LLM-based enrichment pipeline (categorization, attribute extraction, translation), item representation models and embeddings powering search and recommendations, ML-assisted moderation replacing manual review, and the labeling and evaluation platform underpinning all model work.
Key responsibilities include owning the end-to-end product data pipeline with clear SLAs for freshness, coverage, and quality; leading the ML roadmap for catalogue intelligence (category trees, attribute coverage, translation quality, moderation, embeddings, recommendations); driving large cross-team projects to production; contributing to quarterly planning and OKR definition; reviewing feature designs for ML evaluation, inference cost, latency, and data residency compliance; building and maintaining evaluation and labeling infrastructure; managing technical debt and incidents; hiring and developing ML engineers into business-outcome owners; fostering cross-team collaboration with Shopping, Offers, Monetisation, catalogue operations, and partner support; and ensuring transparency and process optimization.
You will work in a results-oriented, business-focused culture and report on key team performance indicators.
Tech stack: Python, Go, PostgreSQL, Pub/Sub, BigQuery, GCS, Kubernetes, Google Cloud Platform, Airflow, microservices architecture.
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), making them observable, reproducible, and reliable
- Solid backend fundamentals; comfortable reviewing Go and Python services and reasoning about distributed systems
- Strong grasp of ML evaluation: golden datasets, labeling workflows, offline metrics, and A/B testing tied to business outcomes
- Product sense: connecting catalogue quality to conversion, discovery, and merchant growth; ability to prioritize accordingly
- Proactive mindset and ability to work independently
- Strong English communication skills (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
