Emplacement

Paris
France
France

Contrat

À plein temps, Durée indéterminée

Date de publication:

2026-11-10

Online to:

2027-01-09
EUR 15.00 HOUR

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Senior ML Engineer - Offline Team

Voodoo
France
4 heures il y a

h3About Voodoo /h3p Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. /ph3Team /h3p 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. /pp 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#39;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. /pp 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#39;s financial performance. It#39;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. /pp strongThis role can be either Paris or Helsinki based and done in a hybrid setup. /strong /ph3Role /h3p We#39;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. /pp In this role, you will be leading the following topics: /pul no-style="min-height:1.5em"lip 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. /p /lilip 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). /p /lilip 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. /p /lilip Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. /p /lilip ML Lifecycle Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. /p /lilip 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. /p /lilip 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. /p /lilip 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. /p /li /ulh3Profile /h3p 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. /pul no-style="min-height:1.5em"lip 5+ years minimum of experience as an ML Engineer or a similar role /p /lilip End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration /p /lilip Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs /p /lilip Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping /p /lilip ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning /p /lilip Performance Optimization: Experience optimizing memory usage during data preprocessing and model training /p /lilip Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) /p /lilip Cloud Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics /p /lilip 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.) /p /lilip Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus /p /lilip Excellent communication skills in English /p /li /ulh3Our Stack /h3p Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services /ph3Benefits /h3ul no-style="min-height:1.5em"lip Excellent benefits that will depend on the country you#39;re based in /p /li /ulp h3Location /h3pHelsinki; Paris /p /pp h3Employment Type /h3pFull time /p /pp h3Location Type /h3pHybrid /p /pp h3Department /h3pEngineering Data /p /p

Postuler

Senior ML Engineer - Offline Team

Emplacement

Contrat

Date de publication:

Online to:

Senior ML Engineer - Offline Team

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