Machine Learning Engineer - Applied ML & Research

Superbet; Madrid; Presencial; EUR 40.000 - 65.000; Jornada completa

Descripción de la vacante

Superbet is looking for a Machine Learning Engineer to join their Applied ML & Research team in Madrid, Spain. In this role, you will develop innovative machine learning solutions that enhance security and user experience across their gaming platforms. Ideal candidates have a Bachelor’s degree in a relevant field and 2+ years of experience, with proficiency in Python and familiarity with ML libraries like PyTorch and XGBoost. You will collaborate with product and engineering teams to drive impactful ML initiatives.

Formación

• 2+ years of industry experience building and deploying ML systems.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs).
• Demonstrated ability to write maintainable, tested code and follow engineering best practices.

Responsabilidades

• Partner with product and engineering to identify and execute machine learning use cases.
• Design, build, and iterate on machine learning solutions.
• Implement reliable training/inference pipelines and improve reproducibility.

Conocimientos

Python

Machine Learning

Data Science

Problem-solving

Educación

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

Herramientas

PyTorch

XGBoost

SQL

AWS

Descripción del empleo

Machine Learning Engineer

As a Machine Learning Engineer in our Applied ML & Research team, you will drive the development of cutting‑edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large‑scale data‑driven decision‑making for hundreds of thousands of users daily.

Responsibilities

• Partner with product and engineering to identify and execute machine learning use cases that deliver measurable impact.
• Design, build, and iterate on machine learning solutions (e.g., classifiers, regressors, ranking/retrieval, and rule‑based components).
• Contribute across the ML lifecycle: data exploration, feature engineering, training, evaluation, deployment, and monitoring.
• Implement reliable training/inference pipelines and help improve reproducibility, testing, and observability.
• Communicate model behavior, trade‑offs, and results clearly to both technical and non‑technical stakeholders.
• Contribute to team standards: code quality, documentation, experimentation hygiene, and responsible ML practices.

Qualifications

• Bachelor’s degree in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field (Master’s a plus).
• 2+ years of industry experience building and deploying ML systems.
• Solid proficiency in Python and familiarity with common ML libraries (e.g., PyTorch, XGBoost) and SQL.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• Demonstrated ability to write maintainable, tested code, participate in code reviews, and follow engineering best practices.
• Strong problem‑solving skills with the ability to break down ambiguous problems into scoped tasks and deliver iteratively.

Bonus Points

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands‑on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open‑source ML projects.