О компании
Voodoo creates mobile games and apps, including games such as Mob Control and Block Jam and apps such as BeReal and Wizz. Its Engineering & Data team builds technology products and platforms for its gaming and consumer apps. The Voodoo Ad-Network builds advertising network services and uses first-party data to optimize and scale monetization.
Задачи
•Take end-to-end ownership of projects from ideation to production release, including scoping features, estimating timelines, designing architecture, and benchmarking technologies
•Build and maintain data and ML pipelines; optimize training pipelines for performance, memory efficiency, and cost
•Provide reusable, tested pipeline components for Data Scientists and review contributions to shared code
•Extend dataset capabilities by integrating data sources, scaling feature windows, and increasing training data volumes within pipeline constraints
•Contribute to deep learning development, including GPU workload orchestration, custom PyTorch training loops, and model architecture support
•Maintain reproducibility and consistency across the ML lifecycle through versioned configs, experiment tracking, and online-offline consistency tooling
•Collaborate with infrastructure teams on scalability, node pools, resource monitoring, and CI/CD migrations
•Participate in a weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems
•Collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers
•Share knowledge and contribute to an engineering culture where team members feel supported and empowered to grow
Требования
•At least 5 years of experience as an ML Engineer or in a similar role
•Experience with end-to-end ML ownership, including problem framing, baselines, experimentation, deployment, and iteration
•Extensive Python knowledge for ML pipeline code, including preprocessing, training and evaluation workflows, experiment utilities, and reproducible configs
•Ability to implement training mechanics, including custom steps and 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, and reproducible training using configs, seeds, and versioning
•Experience with Amazon Web Services and familiarity with scalability, reliability, and security
•Understanding of production ML challenges, including feature stores and training-serving skew
•Excellent English communication skills
Будет плюсом
•Experience with model-serving infrastructure, including real-time or batch inference and latency or throughput optimization
Условия
Benefits depend on the country of residence.
