Role: Machine Learning Engineer
Location: Dubai Department: Technology & Operations Reports To: CTOPosition: Full-Time
About us:Global Drum is empowering the media sector to own, understand, and monetise their most valuable social audiences, using live audience behavioral data for brands to operate diversified business models on demand for themselves.
We are evolving our unique next-generation B2B2C Platform as a Service (Paa S) that operates on a globally distributed cloud infrastructure incorporating a scalable, event-driven architecture using the latest AI and data techniques to evolve an entire sector.
Located in London, Dubai, New York and Dubai, the Company is at the centre of reshaping how global brands will evolve in a sector valued at $276Bn in 2025 representing 30% of all digital ad spending. If this seems too ambitious for you, then don't apply.
Job Description:We are looking for a visionary Machine Learning Engineer to become our first-ever ML hire, embedding data science and applied ML into the heart of our AI-native, multi-tenant platform. You will work directly with the Director of Engineering and product owners to turn audience, content, and revenue data into models that power recommendations, audience understanding, and monetisation decisions across the platform. This is a rare opportunity to define the ML function from the ground up: choosing the stack, setting the standards, and building the first production models that the rest of the engineering org will be built around.
Key Responsibilities:Own Recommendations End-to-End: Design, build, and iterate on recommender systems that surface content and audience opportunities to our media partners. Build the ML Foundation: Establish the first ML pipelines, feature stores, experimentation frameworks, and model-serving infrastructure for the platform, since none currently exist. Data-to-Product Pipeline: Partner with the engineering team to design real-time, event-driven data pipelines that feed model training and low-latency inference. Experimentation & Evaluation: Define offline and online evaluation methodology (A/B testing, holdout sets, ranking metrics) to validate model impact on engagement and revenue. Set ML Standards: Establish best practices for model development, versioning, reproducibility, monitoring, and responsible use of data Stay Hands-On: Write production-grade ML code, heavily leveraging modern AI coding assistants and agentic development workflows to move fast as a team of one (initially). Cross-Functional Partnership: Work closely with the Engineering and platform/revenue product owners to translate business and product questions into ML-solvable problems. Scale the Function: As the first ML hire, help define the roadmap and hiring plan for the ML team as it grows.
Required Skills and Qualifications:Proven, hands-on experience building and deploying recommender systems in production Working knowledge of graph-based learning, including Graph Neural Networks. Solid understanding of network science fundamentals. Experience designing and training models on large-scale, real-world data, including handling sparsity, and skewed engagement distributions. Comfortable owning the full ML lifecycle: feature engineering, training, evaluation, deployment, and monitoring Fluency with modern ML tooling (e.g., PyTorch/Tensor Flow and standard MLOps practices). Experience using AI coding assistants and agentic workflows to accelerate development. Track record of working effectively in an early-stage startup environment.
Bonus Points If You Have... Exposure to the media and/or news technology ecosystem. Experience in AdTech: advertising networks, ad serving, audience segmentation.
Preferred Experience/Qualifications:Strong domain knowledge in Saa S/Paa S, Mar Tech, or AdTech is highly valued. Experience building models that directly optimize platform revenue outcomes. Experience building recommendation systems in production using LLMs and VLMsComfortable being the sole ML voice in the room initially, while communicating clearly with engineering and product stakeholders who may not have deep ML background
Why Join Us? The chance to build a ground-breaking AI platform from day one Competitive equity options package and salary Flexible working environment