18 days ago
Helsinki, Finland or Paris, FranceSenior
Responsibilities
- Own ML infrastructure projects end to end, including scoping, estimation, architecture, benchmarking, and production release.
- Build and maintain data and machine learning pipelines, optimizing performance, memory usage, and cloud costs.
- Develop reusable, tested pipeline components and enable data scientists to work effectively with shared code and datasets.
- Contribute to deep learning development through GPU orchestration, custom PyTorch training loops, and model architecture support.
- Maintain reproducible ML workflows with versioned configurations, experiment tracking, and online-offline consistency tooling.
- Collaborate on scalability, node pools, resource monitoring, and CI/CD migrations.
- Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems.
- Mentor teammates and contribute to a supportive engineering culture.
Requirements
- At least 5 years of experience as an ML Engineer or in a similar role.
- Experience owning the end-to-end ML lifecycle, including problem framing, experimentation, deployment, and iteration.
- Extensive Python experience for preprocessing, training and evaluation workflows, experiment utilities, and reproducible configurations.
- Ability to implement custom training mechanics, metrics, data-loading patterns, and performance-conscious preprocessing.
- Practical experience training and evaluating models with scikit-learn, LightGBM, and PyTorch.
- Experience optimizing memory usage during data preprocessing and model training.
- Experience with hyperparameter tuning, experiment tracking, reproducible training, configuration management, seeds, and versioning.
- Experience with Amazon Web Services and familiarity with scalability, reliability, and security topics.
- Understanding of production ML challenges such as feature stores and training-serving skew.
- Experience with real-time or batch model-serving infrastructure and latency or throughput optimization is a plus.
- Excellent communication skills in English.
Benefits
- Benefits vary depending on whether the role is based in Paris or Helsinki.
- Hybrid work setup.
Tech Stack
Apache AirflowApache SparkAWSdbtKubernetesLightGBMMLflowPrometheusPythonPyTorchscikit-learnTerraform
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