Top Machine Learning Jobs in Lisbon

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Build Superhuman’s AI agent platform, including orchestration, routing, planning, and multi-agent workflows. Develop and deploy machine learning systems for search ranking, proactive suggestions, and other AI-native product experiences. Integrate modern LLMs, contribute strong product judgment, and move rapidly from experimentation to production while working autonomously with cross-functional teams.

Leads the product strategy, roadmap, and scaling of Mastercard’s Global Portfolio Optimization capabilities. Converts analytics and business insights into technology-enabled products, recommendations, and workflows through the Mastercard Intelligence Centre. Defines the organization’s AI strategy, evaluates automation and emerging technologies, develops business cases and KPIs, and guides cross-functional teams through delivery, adoption, and optimization. The role requires strong product leadership, technical fluency, quantitative reasoning, stakeholder management, and the ability to drive outcomes in a matrixed organization.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Lead the architecture, integration, scalability, reliability, and governance of enterprise AI systems. Establish technical standards, advise on AI solution design, oversee specifications and testing, manage cloud infrastructure performance and costs, coordinate cross-functional teams, and mitigate technical risks. The role requires strong experience in DevOps, cloud architecture, AI tooling, data platforms, infrastructure as code, CI/CD, containers, and major cloud providers.

Design AI-powered user experiences across web, desktop, mobile, and administrative products. Lead UX strategy, research, interaction design, prototyping, usability testing, and post-launch optimization for conversational interfaces, copilots, recommendations, automation, and agentic workflows. Establish trustworthy, transparent, accessible AI interaction patterns and contribute to scalable design systems. Partner with Product, Engineering, AI/ML, Research, Security, Privacy, and Legal teams while mentoring designers and influencing product strategy.

Design and lead trusted AI experiences across Intermedia products, including copilots, conversational interfaces, recommendations, automation, and agentic workflows. Own initiatives from research through launch, creating journeys, prototypes, high-fidelity designs, and design-system patterns. Collaborate with Product, Engineering, AI/ML, Research, Security, Privacy, and Legal teams while establishing transparency, user control, accessibility, and responsible AI practices. Mentor designers and help shape the organization’s overall AI interaction language.

Design trusted, intuitive AI experiences across Intermedia products, including copilots, assistants, conversational interfaces, recommendations, automation, and agentic workflows. Lead initiatives from research and discovery through prototyping, implementation, launch, and optimization. Collaborate with product, engineering, AI/ML, research, security, privacy, and legal teams; contribute to design systems; establish responsible AI patterns; conduct user research and usability testing; and mentor other designers.

Develop machine learning and AI security solutions to detect anomalous activity, abuse, fraud, and security risks. Analyze large behavioral datasets, build and improve detection models, evaluate Zendesk AI features, and guide production implementation. Collaborate with security, engineering, research, and product teams; mentor colleagues; monitor model performance; and translate technical findings into actionable prevention strategies.

Develops industrial engineering systems and tools for cycle-time calculations, productivity incentives, capacity planning, optimization, simulation, material flow, and data-driven analysis. Supports and trains operational industrial engineers, validates IT systems, benchmarks standards, balances capacity, and explores machine learning applications for plant constraints, setup prediction, resources, staffing, and KPIs. The position is site-based and requires engineering, statistics, project management, programming, analytics, and AI/ML knowledge.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Leads clinical data science delivery and team development, designing advanced statistical models and machine learning methods for clinical research. Oversees data analysis, management, regulatory compliance, and trial optimization while collaborating with clinical, biostatistical, and regulatory teams. Communicates complex insights to senior stakeholders and external partners and drives innovation across clinical development programs.

Lead technical discovery, AI readiness assessments, solution design, demos, pilots, and proofs of value for Zendesk CX and employee service solutions. Architect secure, scalable integrations using APIs, middleware, telephony, and cloud platforms. Translate AI and automation capabilities into business outcomes, guide technical strategy throughout the sales cycle, demonstrate ROI, support AI adoption, and collaborate cross-functionally on product and go-to-market strategy.

Own end-to-end talent acquisition and headhunting for Arago’s technical hiring across France, North America, and Israel. Partner with the CEO, CTOs, and engineering leaders to define roles, source and close senior engineers, scientists, and technical leaders across hardware, optical, software, and research. Build proactive sourcing pipelines, improve interview processes and hiring standards, monitor pipeline health and hiring velocity, and communicate progress to leadership.

Owns the technical foundation of the CRM ecosystem, including system stability, workflow orchestration, data flows, QA, automation, templates, and data quality. The role mentors CRM team members, supports campaign execution, collaborates with Engineering, MarTech, and Data Science, and contributes to integrations, tracking, and machine-learning-driven experiments.

Leads demand-side yield strategy across iion’s ad marketplace and owns strategy for Cognitiion, its AI-driven yield optimization platform. Diagnoses marketplace performance, directs Yield Analysts, designs experiments, defines optimization metrics, and converts findings into platform requirements. Partners with marketplace, commercial, data science, engineering, and AI leadership to improve revenue, efficiency, bidding, win rates, and traffic allocation.

Lead Essity’s AI technical architecture strategy and roadmap, translating business goals into scalable end-to-end AI solutions. Partner with business leaders, data scientists, AI engineers, and technology providers to prioritize and pilot use cases. Evaluate AI technologies, conduct data/model/software audits, ensure compliance with AI risk standards, and support model recalibration and retraining.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Build and maintain Ergodic AI’s AI platform across back-end and front-end systems. The role involves developing with Python web frameworks and JavaScript/TypeScript, integrating with client technology stacks and workflows, applying AI, ML, and LLM concepts, and contributing to DevOps, cloud deployment, testing, and infrastructure practices. As an early-stage hire, the engineer will help shape the product and solve complex data and AI challenges.

Build and ship production AI systems, including LLM agents, retrieval pipelines, event intelligence, tool integrations, and inference services. Contribute code, tests, observability, evaluation, and monitoring for high-volume real-time systems. Collaborate with platform, product, and applied-research teams while learning to optimize latency, throughput, cost, reliability, and model quality through mentorship and code reviews.

Lead the architecture, full-stack development, delivery, scalability, and reliability of an invoicing and accounting platform. Own backend C#/.NET, frontend Vue.js, Azure infrastructure, testing, deployment, and AI integration. Resolve technical challenges, guide architectural decisions, support scalable AI workloads, and shape the company’s technology direction while communicating effectively with technical and non-technical stakeholders.

Lead technical data science projects from scoping and proof of concept through production deployment. Develop robust machine learning models, collaborate with data engineering and MLOps teams, engage stakeholders, translate technical concepts, and mentor junior data scientists. The role requires Python and SQL expertise, advanced machine learning knowledge, cloud and big data platform experience, exploratory analysis, dashboarding, data engineering tools, and model deployment experience.