Top Machine Learning Jobs in Lisbon

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

As an AI/ML Engineer, you will develop and optimize machine learning models for mobile applications, collaborate with teams, and analyze data to enhance user experience.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

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

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

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.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Design, deploy, and maintain scalable ML pipelines and infrastructure. Build CI/CD for models, automate deployment/monitoring/rollback, implement model performance monitoring and feedback loops, integrate models with data teams, and provide technical leadership and mentorship.

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.

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.

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.