Top Machine Learning Jobs in Portugal

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

Design, deploy, and maintain scalable ML pipelines and infrastructure. Implement CI/CD, automation, monitoring, and model lifecycle management. Collaborate with data scientists and engineers, provide technical leadership, and ensure reliable production integration of ML models using cloud and container orchestration technologies.

As a Machine Learning Engineer, you will build and productionize ML models, monitor performance, integrate models into data pipelines, and collaborate with software engineers on intelligent services.

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

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.

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

As a ML Ops Engineer, you will design, deploy, and maintain ML pipelines, ensuring integration of models into production and optimizing performance.

Join the AI engineering team to develop reliable AI infrastructure for manufacturing systems, focusing on MCP servers, telemetry, and model retraining pipelines.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

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

As a Full Stack Data Scientist, you will analyze data, create models, build dashboards, and support cross-functional teams to drive data-driven decisions and operational efficiency at Bounce.

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.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

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.

Lead end-to-end ML systems for LLM/NLP document intelligence and supply-chain predictive analytics. Design and fine-tune transformer-based models, implement RAG pipelines, build forecasting, classification and optimization algorithms, and productionize models using containerization and MLOps tools. Ensure robust, explainable models deployed to secure on-premise environments with monitoring and continuous improvement.

Lead and scale an ML Engineering team to productionize models on a central MLOps platform; define ML engineering standards, drive technical strategy and architecture across data science and platform teams, mentor engineers, and ensure alignment with business objectives.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams, and analyze data to enhance user experience.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.