Top Machine Learning Jobs in Lisbon

Lead a product vertical end-to-end for a neobank superapp: define strategy, roadmap, and acceptance criteria; shape technical design for money movement, reliability, and compliance; apply and evaluate AI/LLM features; coordinate engineering, design, compliance, ops and finance to ship secure, auditable financial products.

Lead cross-functional projects from planning to delivery, improve operational processes, analyze budgets and KPIs, create internal communications, and introduce AI and automation to increase team efficiency and engagement.

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.

Design, develop, and deploy production-ready AI solutions for insurance, focusing on agentic AI, multi-agent systems, LLMs, RAG pipelines, and computer vision. Research and implement state-of-the-art models, take prototypes through production, collaborate with data scientists and engineers, and contribute to proprietary insurance-focused AI models and scalable international AI products.

Design, develop and deploy machine learning and deep learning (including genAI) models to improve credit risk data quality. Perform EDA, feature engineering, model evaluation, automation, and collaborate with stakeholders to translate business needs into data-driven solutions while ensuring regulatory compliance and coordinating monitoring committees.

Own and improve the technical CRM ecosystem: ensure stability and performance, optimize data flows and automation, redesign templates and components, support campaign execution, and partner with Engineering, MarTech, and Data Science to enable tracking, integrations, and ML-driven experiments while mentoring the CRM team.

Design, build, and maintain enterprise-grade UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot across environments, collaborate with architects and DevOps, and mentor junior engineers while supporting delivery planning and technical client discussions.

Design, build, and maintain enterprise UiPath automations including orchestration, exception handling, monitoring, security, and integrations. Translate architectures into reusable frameworks, review code, troubleshoot issues, mentor engineers, and support delivery in Agile environments.

Design, develop, and deploy generative AI models for business use cases. Collaborate with business and technical teams to identify AI opportunities, conduct research on generative AI advancements, and integrate innovations into production solutions.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Lead and scale a regional Market & Customer Intelligence program across EurAsia, turning market and customer insights into actionable, AI-enabled analytics and decision-ready narratives. Own rollout roadmaps, governance, intelligence cycles, stakeholder enablement, KPI tracking, and cross-functional adoption to embed the capability as a trusted enterprise asset.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Lead and grow multiple small, high-output engineering teams focused on AI-powered vulnerability management. Stay hands-on with architecture and code reviews, drive hiring and career development, ensure technical excellence, and coordinate cross-team priorities to deliver customer and revenue impact.

As an AI Product Manager, you will lead the development of AI-powered features, collaborating with cross-functional teams to align products with user needs and business goals, while overseeing the product lifecycle.

Lead product strategy and execution for AI-enabled insurance products. Define 6–12 month direction, own P&L-impacting programmes, embed experimentation and AI playbooks, mentor PMs, and partner across engineering, data science, design, legal, and leadership to deliver measurable commercial outcomes.

You will solve complex problems related to cross-border logistics and B2B systems, focusing on high-load architecture and AI-driven tools.

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.

Design, deploy, and maintain production-grade ML systems for revenue management. Build data pipelines, implement and validate models, establish MLOps practices (CI/CD, orchestration, monitoring), define SLIs/SLOs, and collaborate with product and engineering to drive hotel pricing and revenue optimization.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.