Machine Learning Engineer

Role Overview

Role Details

• The role is based in Dubai.
• The position reports to the CTO and sits within Technology and Operations.
• The position is full-time.
• The company describes the working environment as flexible.

About GlobalDrum

Core Responsibilities

• Design, build, and iterate on recommender systems for media partners.
• Establish ML pipelines, feature stores, experimentation frameworks, and model-serving infrastructure.
• Design real-time, event-driven data pipelines for model training and low-latency inference.
• Define offline and online evaluation methods, including A/B testing, holdout sets, and ranking metrics.
• Establish standards for development, versioning, reproducibility, monitoring, and responsible data use.
• Write production-grade ML code using AI coding assistants and agentic workflows.
• Translate business and product questions into ML-solvable problems.
• Help define the ML roadmap and hiring plan as the function grows.

Required Qualifications

• Proven hands-on experience building and deploying recommender systems in production is required.
• Working knowledge of graph-based learning, including Graph Neural Networks, is required.
• Solid understanding of network science fundamentals is required.
• Experience with large-scale real-world data, sparsity, and skewed engagement distributions is required.
• Full ML lifecycle ownership from feature engineering through monitoring is required.
• Fluency with modern ML tooling such as PyTorch or TensorFlow and standard MLOps practices is required.
• Experience using AI coding assistants and agentic workflows is required.
• A track record in an early-stage startup environment is required.

Preferred Experience

• Exposure to the media or news technology ecosystem is a bonus.
• AdTech experience in advertising networks, ad serving, or audience segmentation is a bonus.
• SaaS/PaaS, MarTech, or AdTech domain knowledge is highly valued.
• Experience optimizing platform revenue outcomes is preferred.
• Experience building production recommendation systems using LLMs and VLMs is preferred.

Technical Focus

• The role covers recommender systems, graph learning, network science, feature engineering, model evaluation, deployment, and monitoring.
• The role uses modern ML tooling, MLOps practices, feature stores, experimentation frameworks, and model-serving infrastructure.

Why Join

• The role offers the opportunity to build an AI platform from day one.
• The company mentions competitive equity options and salary.
• The working environment is described as flexible.