Engineering Manager (ML)

AI in this role

fine-tuningnlp

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.

What you’ll bring:

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

Responsibilities:

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

How we rate this

Engineering Manager (ML) at Tabby rates 91 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningNLP

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Fine Tuning and NLP. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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