Senior ML Engineer – Offline Team

Voodoo

Senior ML Engineer building and maintaining ML training infrastructure for ad-targeting models at Voodoo. Collaborating with Data Engineers and Data Scientists in a growing tech team.

Posted 5/11/2026full-timeHelsinki • 🇫🇮 FinlandSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudNode.jsPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Excellent benefits that will depend on the country you're based in

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdeep learningPythonscikit-learnLightGBMPyTorchperformance optimizationexperiment managementdata preprocessingmodel serving

Soft Skills

communicationmentorshipcollaborationagile methodologyproblem framingteam culturesupportive environmentdecision-makingknowledge sharingempowerment