ML Engineer: Dynamic Pricing & Recommender Pipelines Remote

HireHi; Barcelona; Híbrido; EUR 70.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

25 Days of paid holiday

20 Remote working days per year

Private medical cover

Life and critical illness cover

Employee Assistance Program

Sport compensation

Meal and transport vouchers

English and Spanish classes

Continuous professional development

Descripción de la vacante

Tripledot Studios is looking for a hands-on ML engineer to build and refine training pipelines for dynamic pricing and recommender systems. You will design features, label schemas, and evaluation metrics, ensuring robust offline and online validation.

You will work with monetization and product teams to connect model outcomes to revenue, monitor performance, and explore AI-powered tools to boost developer productivity while maintaining production quality.

Formación

• Hands-on experience training and evaluating tabular models using neural networks or gradient-boosted trees.
• Experience designing features and labels, choosing metrics for model decisions, and judging when offline results warrant an A/B test.
• Proficiency in SQL and ability to write efficient queries to extract, manipulate, and aggregate data from relational databases.
• An investigative approach to incomplete data and longer-term requirements that need clarification.
• Ability to explain model results to monetization and product partners in terms of revenue and player outcomes.
• Ability to participate in English meetings, read and write work documentation independently, and communicate clearly in English at B2 level or above.
• Nice to have: experience in ad tech, recommender systems, or online marketplaces; production APIs or ML model deployment; the Ray framework.

Responsabilidades

• Build and improve training pipelines for dynamic pricing and recommender system models, from feature and label design through training, tuning, and offline evaluation of tabular models.
• Monitor model performance across games and products, investigate data and concept drift, and make models easier to extend to other products.
• Diagnose missed model outcomes across the training pipeline, business logic, and underlying data, and implement improvements to restore model quality.
• Work with monetization and product colleagues to connect model decisions to revenue and player outcomes and plan A/B tests.
• Identify gaps in the team’s understanding of the models and propose improvements to their direction.
• Use AI-assisted development tools, including code assistants and LLM-based copilots, to accelerate implementation, debugging, and iteration while maintaining production-quality standards.
• Critically review and validate AI-generated code, model implementations, and infrastructure configurations for reliability, correctness, and maintainability.
• Explore AI-powered approaches to improve developer productivity or ML platform capability.

Conocimientos

Tabular models

Neural networks

Gradient-boosted trees

Feature design

SQL

English communication

Ad tech / recommender systems

ML deployment

Ray framework

Herramientas

Ray framework

Descripción del empleo

Tripledot Studios is looking for a hands-on ML engineer to build and refine training pipelines for dynamic pricing and recommender systems. You will design features, label schemas, and evaluation metrics, ensuring robust offline and online validation.

You will work with monetization and product teams to connect model outcomes to revenue, monitor performance, and explore AI-powered tools to boost developer productivity while maintaining production quality.