The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup.
Role
We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics:
Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. # Profile
We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role
End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration
Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs
Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping
ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning
Performance Optimization: Experience optimizing memory usage during data preprocessing and model training
Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning)
Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics
ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.)
Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus
