Top Machine Learning Jobs in Lisbon

Lead the design and implementation of scalable AI and ML solutions. Collaborate with teams to develop architecture and MLOps best practices, ensuring AI governance and operational excellence.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

The Principal AI/ML Scientist & Engineer will lead a team, focusing on AI/ML strategy and developing data products for business outcomes.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

The intern will develop AI models, enhance neural mechanism understanding, and collaborate on generative modeling while maintaining code quality.

The Senior Machine Learning Engineer will lead the development and maintenance of ML infrastructure and pipelines, ensuring smooth operations of ML projects and implementing MLOps best practices in collaboration with cross-functional teams.

The Senior Data Scientist will develop and oversee machine learning models, collaborate with engineering teams, and lead data science initiatives to optimize and expand solutions.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior Machine Learning Engineer will design, develop, and deploy advanced ML solutions, focusing on LLMs and deep learning, while collaborating with cross-functional teams to deliver impactful business outcomes.

Develop and maintain infrastructure for machine learning models, automate MLOps processes, and collaborate with teams to enhance data science capabilities.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.