Top Machine Learning Jobs in Lisbon

The Lead Machine Learning Engineer will optimize pricing processes, manage ML model lifecycles, and collaborate with cross-functional teams to enhance predictive modeling capabilities.

The AI/ML Engineer will design and deploy AI pipelines, integrating LLMs into production, and develop scalable machine learning systems while collaborating with stakeholders and data engineers.

Responsible for building, deploying, and maintaining scalable machine learning solutions, implementing end-to-end ML pipelines, and ensuring model production quality.

Responsible for building, deploying, and maintaining scalable machine learning solutions, ensuring the transition from prototypes to production. Duties include designing ML pipelines, managing automated CI/CD processes, fine-tuning model performance, and collaborating with Data Scientists.

The role involves developing scalable AI solutions, optimizing pipelines, mentoring engineers, and collaborating in a cross-functional team to enhance data insights for life sciences.

As an MLOps Engineer, you will design, deploy, and maintain ML pipelines, integrate models into production, and automate processes for reliability. Collaborate with data scientists and engineers while providing mentorship.

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

The DS/ML Engineer at Veeva will develop and optimize data-driven applications, use AI for business workflows, and mentor team members, ensuring high-quality ML solutions.

The Senior ML Scientist is responsible for researching and building machine learning solutions for AI capabilities, collaborating closely with teams to improve customer support systems using ML techniques.

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

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

Develop and evaluate scalable deep learning algorithms, collaborate on generative modeling, and improve data processing pipelines to enhance understanding of neural mechanisms.

Design and build cloud-native, low-latency data and ML infrastructure; implement state-of-the-art research into backend services; write testable code; communicate results via papers/patents; collaborate with Product and Data Science.

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.

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

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.

Design and implement machine learning pipelines, working closely with data scientists to create scalable models and production-grade code.

As a Machine Learning Engineer, you will develop scalable ML pipelines, maintain microservices for model hosting, and support CI/CD processes.

The Senior Machine Learning Engineer role involves designing and implementing ML pipelines and systems, collaborating with data scientists, and managing model deployment and diagnostics.

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Design and improve AI/ML solutions, develop data processing pipelines, collaborate with teams, provide mentorship, and promote best practices in AI/ML.

Develop personalized product feeds for users by analyzing data, creating hypotheses, building models, and deploying scalable production code.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.