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Senior ML Engineer - Offline Team

Voodoo· Engineering & Data
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Hybrid (Helsinki), Hybrid (Paris)Full-timePosted 5mo ago

About the role

Senior ML Engineer joining the Models team to build and maintain ML training infrastructure powering ad-targeting models, from data preprocessing to production monitoring, in a hybrid Paris or Helsinki setting.

Responsibilities

  • 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.

Qualifications

  • 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
  • Excellent communication skills in English

Skills

Required:ML Lifecycle ManagementPythonPyTorchScikit-learnLightGBM
Nice to have:Model Serving

Benefits

  • Excellent benefits that will depend on the country you're based in
Last updated Aug 20, 2026. Interested? Apply on the company site.

About Voodoo

Entertain the world

Voodoo is a global tech company that entertains the world through iconic apps and games. We empower bold creators to shape their ideas into hits.

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