ML Engineer, Applied ML & Research for Next-Gen Play

super; España; Presencial; EUR 52.000 - 78.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical / Health Insurance

Open Annual Leave

Employee Assistance Programme

Training & Learning Development

Descripción de la vacante

Super is hiring an ML Engineer to shape the next era of play across Europe and Latin America. You will partner with product and engineering to design and deploy ML solutions that impact millions of users, from data exploration to monitoring in production.

The role emphasizes building reliable training and inference pipelines, documenting models, and contributing to best practices in responsible ML across a global team.

Formación

• Bachelor's degree in ML, DS, Stats, CS or related field.
• 2+ years of industry experience building and deploying ML systems.
• Proficiency in Python and ML libraries (PyTorch, XGBoost) and SQL.

Responsabilidades

• Partner with product and engineering teams to identify and execute ML use cases with measurable impact.
• Design, build, and iterate on ML solutions (classifiers, regressors, ranking, rule-based components).
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, monitoring.
• Implement reliable training and inference pipelines with reproducibility and tests.
• Communicate model behavior and trade-offs to technical and non-technical stakeholders.
• Contribute to team standards: code quality, documentation, experimentation hygiene, responsible ML practices.

Conocimientos

Python

PyTorch

SQL

LLMs

Educación

Bachelor's degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or related field

Herramientas

MLflow

ZenML

Metaflow

Descripción del empleo

Super is hiring an ML Engineer to shape the next era of play across Europe and Latin America. You will partner with product and engineering to design and deploy ML solutions that impact millions of users, from data exploration to monitoring in production.

The role emphasizes building reliable training and inference pipelines, documenting models, and contributing to best practices in responsible ML across a global team.