Top Machine Learning Jobs in Lisbon

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

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

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

Lead and scale a Machine Learning Engineering team to turn data science models into production-grade ML applications on a central MLOps platform. Define ML engineering standards, drive technical strategy and architecture, bridge platform and data science teams, mentor engineers, and foster cross-functional collaboration and continuous improvement.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.

Build and productionize generative AI systems and ML pipelines for unstructured data (documents, transcripts, images). Develop APIs, retrieval/RAG workflows, embedding pipelines, and model monitoring. Collaborate with data scientists and engineers to deploy, monitor, and maintain scalable, secure AI services using cloud and MLOps practices.

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.