Top Machine Learning Jobs in Lisbon

As a Senior MLOps Engineer, you'll design and implement MLOps strategies and pipelines while collaborating to enhance fraud detection systems.

Design and develop AI-driven features to enhance customer experience, execute ML projects, and optimize AI performance while collaborating cross-functionally.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Support digital retail platforms by defining tracking and KPIs, managing the full analytics workflow, building dashboards and reports, analyzing product and user behavior, validating data, and delivering insights to product teams and retailers.

Lead and grow a customer-facing engineering team delivering Feedzai solutions. Act as technical lead on client implementations, mentor engineers, hire talent, optimize processes, ensure delivery quality, coordinate with product and customer success, and drive operational excellence and incident response.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Write, edit, localize and proofread English-language email and social media copy for Google Nordics/Benelux; create social-first assets, run A/B test variants, collaborate with designers, developers, analytics and audience teams, and maintain brand voice and campaign documentation.

The role involves designing and implementing AI features within trading applications, focusing on model optimization, prompt engineering, and collaboration with teams to enhance software capabilities.

The Product Definition Analyst will document business flows, gather requirements, assist in solution design, and support testing for biometrics solutions in airport systems.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Lead design, development, validation, and production deployment of advanced ML models. Drive technical projects, collaborate with Data/MLOps Engineers, translate stakeholder needs, and mentor junior data scientists.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

The Senior Product Manager will define the roadmap for ML-powered features, enhance customer satisfaction, and collaborate with cross-functional teams to launch AI capabilities.

Serve as the bridge between clients and technical teams to deploy and optimize Feedzai's fraud detection platform. Gather requirements, translate compliance needs into specifications, define data mappings, design and configure solutions, lead UAT, deliver training, and drive continuous improvement to enhance alert efficiency and investigative workflows.

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.

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.

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.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

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.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

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.