Top Machine Learning Jobs in Portugal

Design, develop and deploy machine learning and deep learning (including genAI) models to improve credit risk data quality. Perform EDA, feature engineering, model evaluation, automation, and collaborate with stakeholders to translate business needs into data-driven solutions while ensuring regulatory compliance and coordinating monitoring committees.

Own and improve the technical CRM ecosystem: ensure stability and performance, optimize data flows and automation, redesign templates and components, support campaign execution, and partner with Engineering, MarTech, and Data Science to enable tracking, integrations, and ML-driven experiments while mentoring the CRM team.

Design, build, and maintain enterprise-grade UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot across environments, collaborate with architects and DevOps, and mentor junior engineers while supporting delivery planning and technical client discussions.

Design, build, and maintain enterprise UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot issues, mentor engineers, and support delivery in Agile environments.

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.

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.

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.

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

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

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.

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.

Design, develop, and deploy generative AI models for business use cases. Collaborate with business and technical teams to identify AI opportunities, conduct research on generative AI advancements, and integrate innovations into production solutions.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

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.

Build, deploy, monitor, and optimise production-grade agentic and LLM systems to detect and prevent risk and fraud. Design agent orchestration, RAG pipelines, and LLM serving while writing production-quality Python, querying large structured datasets with SQL, and implementing ML evaluation, observability, and graceful degradation.

Design, deploy, and maintain scalable ML pipelines and infrastructure. Build CI/CD for models, automate deployment/monitoring/rollback, implement model performance monitoring and feedback loops, integrate models with data teams, and provide technical leadership and mentorship.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Build and deploy production-ready machine learning models to deliver real-time, asynchronous, and batch predictions. Identify ML opportunities from large web and mobile datasets, implement robust scalable code, collaborate with product and engineering teams, and mentor colleagues to create data products that drive business impact.

Build and deploy production-ready ML models to improve user experience, optimize workflows, and detect abuse. Explore large datasets, identify opportunities, collaborate with product and engineering, and deliver scalable real-time, asynchronous, and batch predictions using cloud and Kubernetes.