Top Machine Learning Jobs in Lisbon

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.

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.

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.

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, 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.

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.

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.

Design and deliver cloud-based AI/ML systems with a focus on database architecture, scalable data pipelines, model training and production, generative and deep learning model customization, HPC algorithm R&D, data preprocessing, storage/retrieval design, and cross-functional infrastructure alignment including edge and HPC resources.

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.

Lead a small senior data engineering team owning the end-to-end data platform: streaming, warehousing, modelling, pipelines, ML lift, and operational excellence. Drive delivery, coach engineers, review technical designs, enforce data contracts and quality, manage costs (FinOps), and partner with Product and leadership to set roadmaps and risk. Build and grow an emerging ML function.

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.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

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.