Top Machine Learning Jobs in Portugal

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.

Lead a cross-functional team in developing and maintaining the MLOps infrastructure at Zyte. Responsibilities include managing distributed systems, optimizing model platforms, and overseeing team operations.

The Senior ML Engineer will enhance fine-tuning methodologies, research inference optimization techniques, and re-implement LLM architectures focusing on distributed training and efficiency.

The AI/ML Engineer will develop and maintain AI algorithms, create data pipelines, collaborate on product features, and ensure a high-quality customer experience.

The Senior ML Engineer will collaborate with ML Scientists, lead discussions on system design, manage ML projects, and develop scalable infrastructure for ML features.

Develop and integrate machine learning solutions for fraud prevention in transactions, analyze data using SQL and Python, and manage the complete lifecycle of ML solutions.

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.

Develop ML and AI products, optimize pipelines, collaborate with teams, mentor junior engineers, and resolve production issues in a high-scale environment.

Lead and manage high-performing teams developing ML/AI platforms, focusing on LLM infrastructure and collaboration across departments.

The AI/ML Engineer will develop AI-driven solutions, analyze data, and build production-grade systems, utilizing Python and relevant ML techniques.

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.

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.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

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

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

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

Research and develop AI/ML models for handwriting recognition and related technologies, collaborating with a multidisciplinary team to deliver innovative features.

The Head of ML will oversee market intelligence, search algorithms, AI assistance, logistics optimization, and engineering AI. Responsibilities include leading an ML team, developing strategies, and coding for high-load applications.

As a Senior Machine Learning Engineer, you will lead ML model development, collaborate with cross-functional teams, and set ML best practices.

As a Staff Machine Learning Engineer, you will develop ML models, collaborate across teams, mentor engineers, and drive innovative features for financial crime solutions.

The role involves designing and deploying ML pipelines, managing data flows, and developing APIs for search and ranking systems in luxury fashion e-commerce.

The Middle Infrastructure Engineer will deploy, scale and maintain AI services, collaborating with ML Engineers and Data Scientists to modernize the platform.

Design and develop AI/ML solutions, lead RAG architecture implementation, automate deployment, and perform advanced data analysis.

The Staff ML Engineer will design and optimize ML platform components, enabling scalable and reliable AI-driven experiences at Zendesk, while collaborating with various engineering functions.