Top Machine Learning Jobs in Lisbon

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.

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, develop and deploy end-to-end ML/AI solutions: data collection/labeling, model training and evaluation, collaborate with data scientists and clients, productionize, monitor and maintain models, and promote data-driven practices.

Design, train, evaluate, and deploy NLP and LLM-based models for document processing, parsing, classification, search, and matching. Own end-to-end ML architecture, run experiments, apply robust development/testing practices, and improve AI tooling and standards.

Design, deploy, and optimize machine learning solutions and production-ready pipelines. Apply MLOps practices, use cloud ML platforms, containerization, and CI/CD. Implement feature engineering, model evaluation and monitoring, and integrate models into APIs or backend systems.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Build and operate production-grade model serving infrastructure, design deployment pipelines (blue/green, canary), implement autoscaling and multi-model serving, optimize GPU utilization and network throughput, set up observability and model registries, manage CI/CD for reproducible deployments, own full ML system lifecycle including on-call support and platform scalability.

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

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.