Top Machine Learning Jobs in Portugal

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.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Build, maintain, and scale Mollie’s cloud ML platform on GCP by writing production-grade Python and Terraform. Deploy and operationalize ML models, create CI/CD pipelines, manage Kubernetes model serving, implement MLOps best practices, set up observability, and support/extend open-source and generative AI tooling.

Design, deploy, and maintain production-grade ML systems for revenue management. Build data pipelines, implement and validate models, establish MLOps practices (CI/CD, orchestration, monitoring), define SLIs/SLOs, and collaborate with product and engineering to drive hotel pricing and revenue optimization.

Own end-to-end ML solutions for real-time industrial IoT telemetry: frame problems, build pipelines, deploy and operate models (forecasting, classification, anomaly detection), monitor drift and retrain, mentor engineers, and apply AI-assisted coding with rigorous quality and MLOps practices.

Own end-to-end delivery of ML-powered features: data pipelines, model integration, serving, monitoring, and iteration in production. Productionise research, build ML infrastructure (serving, inference, LLM integrations, evaluation), improve reliability and cost-efficiency, collaborate with engineers and product, and mentor junior engineers.

The Senior Machine Learning Engineer will build and deploy sophisticated AI systems, focusing on Large Language Models, to drive business value and enhance customer experience through machine learning solutions.

Own and deliver ML-powered product features end-to-end: data pipelines, model integration, serving, monitoring, and iteration in production. Productionise research outputs, build ML infrastructure (model serving, inference pipelines, LLM integrations), improve reliability and cost-efficiency, collaborate with product engineers and scientists, and mentor junior engineers.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Design, build, and improve ML models and production pipelines; collaborate with engineers, data scientists, and PMs; develop experimentation roadmaps; set up reproducible environments; monitor and maintain production models; document models and processes; propose and apply new AI/ML solutions.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.

Lead end-to-end ML systems for a proactive long-horizon assistant: prepare data, train and evaluate models, deploy reliable inference, debug production issues, iterate on performance, and mentor peers to meet latency, accuracy, reliability, cost, and safety targets.

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.

Build and maintain data pipelines and production-grade ML engineering solutions on Azure Databricks. Implement MLOps/CD4ML practices, serve and productionize models, use Spark, Airflow, MLflow, Kafka and Delta Lake, version control with GitHub, and work in Agile teams (Jira).

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Lead research, prototype, and deploy state-of-the-art NLP/LLM models for intent detection, chatbots, routing, and automation. Design experiments (offline/online/A-B), analyze large multilingual datasets, productionize models with engineering partners, monitor model health, and mentor junior scientists while translating research into scalable customer-experience solutions.

Build and operate ML infrastructure and platforms for model training, evaluation, deployment, inference, observability, and continuous improvement. Design scalable, low-latency serving, reproducible pipelines, benchmarking, and tooling to support researchers and engineers in production AI workloads.

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

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design, build, and maintain scalable MLOps infrastructure and automation for model deployment and data pipelines. Implement IaC, CI/CD, monitoring, and collaborate with cloud and business stakeholders to productionize machine learning solutions.