Engineering Manager (ML)

Tabby creates financial freedom in the way people shop, earn and save by reshaping their relationship with money. Over 25 million users choose Tabby to stay in control of their spending and make the most out of their money. The company’s flagship offering allows shoppers to split their payments online and in-store with no interest or fees. Over 70,000 global brands and small businesses, including Amazon, Noon, IKEA, and SHEIN use Tabby to accelerate growth and gain loyal customers by offering easy and flexible payments online and in stores. Tabby generates over $18 billion in annual transaction volume for its partner brands and is the highest-rated, most-reviewed, largest, and fastest-growing FinTech in the GCC region. Tabby launched in 2019 and has since raised +$1 billion in equity and debt funding from global and regional investors, and is now valued at $6,5 billion. About the team Tabby Marketplace is where our users discover what to buy. The Content Quality & Personalisation team owns the data that makes the marketplace work: a catalogue of 25M+ products from thousands of merchants, ingested through feeds and e-commerce plugins (Shopify, Salla, Zid, Amazon and more), then categorised, enriched, translated, moderated and published, largely by ML. You will lead a cross-functional team of ML engineers, backend and frontend engineers, QA and a product analyst. The team runs the LLM-based enrichment pipeline (categorisation, attribute extraction, translation), the item representation model and embeddings that power search and recommendations, ML-assisted moderation that is replacing manual review, and the labeling and evaluation platform behind all of it. You will work closely with the Shopping, Offers and Monetisation teams, as well as catalogue operations and partner support. - 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 - 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 - Full-time B2B contract - Fully remote setup - Up to 20% tax allowance - 22 paid leave days annually - Stock options (ESOP) in a fast-scaling, pre-IPO company - Flexi benefits you can use for wellness, travel, or learning - Work alongside a high-performing, international engineering team in a global fintech unicorn

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; comfortable reviewing Go and Python services and reasoning about distributed systems; Strong English communication.

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.