Machine Learning Engineer

Talantir; Madrid; Presencial; EUR 60 000 - 110 000; Tempo integral

Resumo da oferta

Talantir is hiring a Machine Learning Engineer to join our international team in Madrid. You will design and validate predictive models, develop time‑series forecasting, and drive ML across production pipelines using Python, TensorFlow, PyTorch, and Spark MLlib.

You will collaborate with data scientists, data engineers, and product teams to ensure models are robust, scalable, and aligned with business needs while complying with governance and security standards. English communication is required.

Qualificações

• Bachelor’s or Master’s degree in CS, Data Science, Mathematics, Physics, or a related field.
• Demonstrated experience in predictive modelling, regression, and time‑series analysis.
• Machine learning and deep learning techniques experience.
• Python and related scientific libraries (NumPy, Pandas, Scikit‑learn).
• TensorFlow or PyTorch experience.
• Spark MLlib for distributed model training.
• Deploying models into production environments.
• Hands‑on experience with on‑premise platforms and cloud platforms.
• Curiosity and ability to rapidly learn new technologies, frameworks, and research methods.
• Strong analytical, problem‑solving, and communication skills.
• Ability to work collaboratively in multidisciplinary, international teams.

Responsabilidades

• Design, develop, and validate predictive models, regression algorithms, and time‑series forecasting models with focus on performance and robustness.
• Contribute to the full model lifecycle: research, experimentation, industrialization, deployment, and monitoring.
• Build ML and DL models using Python, Spark MLlib, TensorFlow, and PyTorch.
• Optimize and integrate models into distributed data pipelines on Cloudera, Spark, and DaaS architectures.
• Collaborate with Data Engineers to ensure efficient data ingestion and feature engineering in large‑scale environments.
• Work with Data Architects to ensure compliance with data governance and security standards.
• Partner with Data Scientists to design experiments and improve model quality.
• Contribute to product‑oriented initiatives with Product Managers and customers as needed.
• Apply best practices in MLOps, including CI/CD for ML and model monitoring.
• Ensure compliance with data privacy, security, and governance policies.
• Stay informed on advances in ML/DL and model optimization.
• Participate in agile ceremonies like sprint planning and architecture reviews.

Conhecimentos

Predictive modelling

Time-series analysis

Python

TensorFlow

PyTorch

Spark MLlib

Production deployment

Data governance

Collaboration

Formação académica

Bachelor's or Master’s in CS/DS/Math/Physics

Ferramentas

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Descrição da oferta de emprego

Position Purpose

We are seeking a passionate and motivated Machine Learning Engineer to join our international team as part of a strategic expansion of our AI capabilities. In this role, you will work at the intersection of algorithm research and production-grade model implementation, developing innovative solutions in prediction, regression, time‑series forecasting, and sustainability-driven analytics.

Your work will help strengthen our technological ecosystem and accelerate the development of next‑generation AI solutions across the organization.

As a Machine Learning Engineer, you will collaborate closely with Data Scientists, Data Engineers, Data Architects, Product teams, and—when required—customers, ensuring that models are reliable, scalable, and aligned with both business and technical expectations.

What You Will Be Doing

• Design, develop, and validate predictive models, regression algorithms, and time‑series forecasting models with a strong focus on performance, accuracy, and robustness.


• Contribute to the full model lifecycle: research, experimentation, industrialization, deployment, and monitoring.


• Build machine learning and deep learning models using Python, Spark MLlib, TensorFlow, and PyTorch.


• Optimize and integrate models into distributed data pipelines running on Cloudera, Spark, and Data-as-a-Service (DaaS) architectures.


• Collaborate with Data Engineers to ensure efficient data ingestion, preparation, and feature engineering in large‑scale environments.


• Work with Data Architects to ensure algorithmic solutions comply with architectural principles, data governance practices, and security standards.


• Partner with Data Scientists to design experiments, evaluate feature sets, and improve model quality.


• Contribute to product‑oriented initiatives by working with Product Managers and occasionally customers to ensure models address real business needs.


• Apply best practices in MLOps, including CI/CD for ML, model monitoring, drift detection, and automated retraining.


• Ensure strict compliance with data privacy, security, and governance policies across all algorithmic developments.


• Stay informed on the latest advances in machine learning, deep learning, and model optimization techniques.


• Participate in agile ceremonies such as sprint planning, architecture reviews, and continuous integration/deployment activities.

What You Need For This Position

Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, or a related field.

Demonstrated experience in:

• Predictive modelling, regression, and time‑series analysis


• Machine learning and deep learning techniques


• Python and related scientific libraries (NumPy, Pandas, Scikit‑learn)


• TensorFlow or PyTorch


• Spark MLlib for distributed model training


• Deploying models into production environments


• Hands‑on experience or strong motivation to work with on‑premise platforms and cloud platforms.


• Curiosity and the ability to rapidly learn new technologies, frameworks, and research methods.


• Strong analytical, problem‑solving, and communication skills.


• Ability to work collaboratively in multidisciplinary, international teams.

English is a must — you must be able to communicate effectively with global stakeholders.