Senior ML Engineer – Dynamic Pricing & Recommenders

Greenhouse Software, Inc.; Bellprat; Híbrido; EUR 85.000 - 115.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid Working

Private Medical Cover

Life & Critical Illness Cover

Meal and Transport Vouchers

English & Spanish Classes

Continuous Professional Development

Holiday 25 days

Descripción de la vacante

Tripledot Studios is seeking a Senior Machine Learning Engineer to work on dynamic pricing and recommender systems in games. You will focus on tabular ML, including neural networks and gradient-boosted trees, building features and improving models rather than production deployment.

Collaborate with monetization and product teams to plan A/B tests and translate requirements into ML objectives. Expect to contribute to the training pipeline within months and steer model direction by six months.

Formación

• Hands-on training and evaluation of tabular models using neural networks or gradient-boosted trees.
• Experience with PyTorch, PyTorch Lightning, TensorFlow, XGBoost, CatBoost or scikit-learn; no single framework required.
• Proficiency in SQL and writing efficient queries to extract and aggregate data.

Responsabilidades

• Build and improve training pipelines for dynamic pricing and recommender system models, including feature design, training, tuning and offline evaluation.
• Monitor model performance post-launch and investigate data drift and model issues.
• Diagnose missed model outcomes and implement improvements to restore quality.

Conocimientos

Hands-on ML training

Model evaluation

Feature design

Data interpretation

Communicating results

Herramientas

PyTorch

PyTorch Lightning

TensorFlow

XGBoost

CatBoost

scikit-learn

SQL

Ray

Descripción del empleo

Tripledot Studios is seeking a Senior Machine Learning Engineer to work on dynamic pricing and recommender systems in games. You will focus on tabular ML, including neural networks and gradient-boosted trees, building features and improving models rather than production deployment.

Collaborate with monetization and product teams to plan A/B tests and translate requirements into ML objectives. Expect to contribute to the training pipeline within months and steer model direction by six months.