Top Machine Learning Jobs in Lisbon

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

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

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.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Own the Subscriptions roadmap end-to-end: research, prioritize, coordinate cross-product dependencies, drive features through delivery, and enable sales and documentation to maximize retention and revenue.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

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.

Build and productionize document classification and extraction ML systems (including LLM/VLM-based solutions). Design training/evaluation pipelines, optimize GPU inference and latency, lead technical design from RFC to production, drive reliability/observability, mentor engineers, and collaborate cross-functionally to deliver scalable, high-accuracy document fraud detection and verification features.