Top Machine Learning Jobs in Lisbon

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.

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

Own the full sales cycle for enterprise accounts across Central and Eastern Europe: prospect, present and demo Darktrace AI-driven cyber threat defense to CISOs, close large deals, work with partners, SDRs and technical experts, and attend industry events.

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

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.

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

As an Engagement Manager, you'll ensure customer success in protein R&D by building relationships, tracking metrics, and leading cross-functional teams. You'll manage enterprise accounts and advocate for customers while aligning their needs with Cradle's offerings.

Design and maintain scalable data pipelines and ETL/ELT workflows for ML and GenAI, collaborate with teams, and ensure high engineering standards.

The intern will develop AI models, enhance neural mechanism understanding, and collaborate on generative modeling while maintaining code quality.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

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.

Design, deploy and maintain scalable ML pipelines and CI/CD for production models. Build cloud infrastructure with IaC, automate deployments and rollbacks, monitor model performance, and collaborate with data scientists and engineers to standardize workflows and ensure reliability.

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, build, evaluate, and deploy production-grade LLM and classical ML solutions. Create retrieval and agentic workflows, integrate AI into client apps, implement evaluation frameworks, optimize prompts and pipelines, and collaborate with product teams through discovery to production support.

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 operate ML platform systems for training, evaluation, deployment, and serving of generative models. Improve reliability, scalability, performance, observability, and automation for GPU and cloud workloads. Develop internal tooling and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and operational improvements.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

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.

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.

Design, develop, and maintain MLOps infrastructure and scalable deployment pipelines. Build IaC (Terraform), automate CI/CD for ML models, ensure monitoring and reliability, collaborate with cloud and business stakeholders to productionize data science solutions.

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 Machine Learning Engineer will lead the development and maintenance of ML infrastructure and pipelines, ensuring smooth operations of ML projects and implementing MLOps best practices in collaboration with cross-functional teams.

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.