Engineering Manager, ML

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. 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 .

Posted 9/15/2026full-timeRemote • SpainMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the development and management of production ML systems, with a focus on data pipeline optimization, ML evaluation, and cross-functional team leadership. Proven ability to connect product quality to business outcomes in fast-paced e-commerce or fintech environments.

Highest-signal resume keywords

Production ML Systems DevelopmentML Team LeadershipData Pipeline ManagementML Evaluation and A/B TestingStrong Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingLarge Language ModelsData Pipeline DesignGo ProgrammingPython ProgrammingML Evaluation MetricsBatch and Streaming Data ProcessingPrompt DesignFine-Tuning Models

Soft Skills

Proactive MindsetIndependent WorkResults-Oriented Culture

Tools & Technologies

PIM SystemsAPI-Based ModelsSelf-Hosted Models

Industry Keywords

E-CommerceMarketplaceFintechContent ModerationData Residency

Tech Stack

Tools & technologies

Distributed SystemsPythonGo

About the role

Key responsibilities & impact

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, including 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 evaluation and labeling infrastructure to measure model changes before production
Oversee technical debt management and incident handling across ML and backend services
Hire, evaluate, and motivate team members; develop ML engineers into owners of business outcomes
Build cross-team collaboration with Shopping, Offers, Monetisation, catalogue operations and partner support
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
Lead a cross-functional team of ML engineers, backend and frontend engineers, QA and a product analyst
Work closely with Shopping, Offers and Monetisation teams, catalogue operations and partner support

Requirements

What you’ll need

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 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
Proactive mindset and 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
Nice to have: experience with Arabic-language content
Nice to have: familiarity with data residency and regulated-data requirements

Benefits

Comp & perks

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