Top Machine Learning Jobs in Lisbon

Lead architectural design and engineering build-out of company-wide MLOps and agentic AI platforms. Drive productionisation of ML, LLMs, RAG, multi-agent systems and GNNs; set engineering standards, CI/CD, evaluation, observability, and coach ML engineers. Translate data science roadmaps into scalable, compliant production systems.

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 design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

As a Machine Learning Engineer, you will build and productionize ML models, monitor performance, integrate models into data pipelines, and collaborate with software engineers on intelligent services.

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.

Design and improve AI/ML solutions, develop data processing pipelines, collaborate with teams, provide mentorship, and promote best practices in AI/ML.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

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.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Build and scale ML-powered search and RAG capabilities at Zendesk scale. Collaborate with product, ML scientists, and engineers to design, deploy, and maintain reliable production services, improve relevance and ranking, optimize indexing pipelines, and mentor teammates while applying experiments and analysis to drive decisions.

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

Lead and scale a Machine Learning Engineering team to turn data science models into production-grade ML applications on a central MLOps platform. Define ML engineering standards, drive technical strategy and architecture, bridge platform and data science teams, mentor engineers, and foster cross-functional collaboration and continuous improvement.

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.

Build and productionize generative AI systems and ML pipelines for unstructured data (documents, transcripts, images). Develop APIs, retrieval/RAG workflows, embedding pipelines, and model monitoring. Collaborate with data scientists and engineers to deploy, monitor, and maintain scalable, secure AI services using cloud and MLOps practices.

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.

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

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.