Top Machine Learning Jobs in Lisbon

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.

Supports the development, commercialization, and growth of products serving Mastercard ecosystem partners. Analyzes market, customer, and performance data; tracks KPIs and adoption metrics; identifies opportunities; and develops actionable recommendations. Partners with product, technology, analytics, and data science teams on AI-enabled capabilities, go-to-market initiatives, partner adoption strategies, dashboards, presentations, and regional ecosystem growth.

Own the strategy, roadmap, and lifecycle of Cloudflare’s enterprise Email Security platform. Lead threat detection investments, Zero Trust integration, customer discovery, competitive differentiation, and global launches. Partner with engineering, threat intelligence, sales, and design teams to address email threats such as business email compromise and phishing. Drive product growth, enterprise adoption, and technical platform evangelism while making data-informed investment decisions.

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 a multinational BDR organization of approximately 18 representatives through team leaders. Owns BDR strategy, playbook development, performance management, metrics, sales technology adoption, process optimization, and cross-functional alignment with Sales, Marketing, and senior leadership. The role combines strategic planning with hands-on operational leadership across European and US markets, driving scalable prospecting, qualification, conversion, and team development.

Develop end-to-end predictive, classification, simulation, and AI models for Airbus People Analytics. Partner with HR, IT, data analysts, and data scientists to identify use cases, communicate model results, and promote data science adoption. The role involves Python, machine learning, deep learning, LLMs, GPU computing, visualization, API integration, and agile product development while maintaining compliance and integrity.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Develop and operate complex, cloud-native applications for the Altium 365 platform. Responsibilities include building scalable infrastructure, distributed high-load systems, APIs, data models, and platform frameworks; optimizing performance; and contributing to identity, authentication, messaging, caching, search, databases, and multi-tenancy. The role also influences architecture across teams and establishes engineering standards, best practices, and technical governance.

Build, deploy, monitor, and improve production machine learning systems for customer and revenue use cases. Develop scalable data and ML pipelines, work with structured and unstructured data, apply modern ML and LLM techniques, and ensure model reliability through testing and validation. Collaborate with data engineers, product managers, and business stakeholders to deliver end-to-end solutions that improve business outcomes.

Execute document onboarding and database management tasks, label identification-document data under strict privacy controls, analyze document performance, troubleshoot issues, test document-verification tooling, document processes, and support second-line inquiries. The role requires accuracy, ownership, analytical thinking, problem-solving, and cross-functional communication, with opportunities to develop technical skills in document verification and machine learning systems.

Develop and maintain NLP and generative AI products, including RAG systems, content generation, and recommendation applications. Responsibilities include dataset selection and annotation, rapid prototyping, experimentation, statistical evaluation, production deployment, and iterative system improvement. The role requires staying current with AI advances, communicating technical concepts to stakeholders, and collaborating across international teams while addressing multilingual challenges.

Design, develop, deploy, and optimize machine learning and AI solutions, including predictive models, foundation models, LLM applications, and generative AI systems. Build scalable data pipelines, apply MLOps and DevOps practices, monitor production models, analyze large datasets, and leverage cloud AI services. Collaborate with engineers, data scientists, product owners, and business stakeholders to deliver measurable outcomes and communicate technical recommendations.

Develops and deploys data science, advanced analytics, and AI solutions to improve business decisions, commercial effectiveness, and productivity. The role partners with commercial, medical, finance, and business teams; builds predictive models, dashboards, automated workflows, customer segmentation, and next-best-action solutions; and supports data governance, privacy, responsible AI, and adoption of data-driven practices.

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.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Senior Data Scientist responsible for solving complex, cross-functional business problems across marketing, revenue operations, finance, and product. The role develops predictive and statistical models, establishes analytical standards, leads analytics initiatives, mentors team members, and delivers forecasts and recommendations that improve company KPIs. The scientist will partner with executives, translate complex analyses into actionable strategy, and own analytics projects from problem definition through implementation.

Own the Vault product roadmap and lifecycle for a sports media asset platform. Lead discovery with customers, define solutions for content ingest, storage, integrations, AI enrichment, metadata, search, rights, and asset monetization. Partner closely with engineering, sales, and customer success, attend customer events, and prioritize product bets that help sports organizations maximize the value of extensive media archives.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Own the strategy, roadmap, delivery, adoption, and continuous improvement of high-impact Digital Finance products. Shape the integration of AI, generative AI, and agentic workflows while partnering with engineering, design, data, Finance, and Digital teams. Use customer insights, experimentation, metrics, and business outcomes to prioritize investments, improve product performance, influence senior stakeholders, and advance AI product management practices across the organization.

Lead data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

Drive enterprise sales growth for IQVIA’s AI-powered pharmacovigilance and drug safety technology across EMEA. Responsibilities include prospecting, developing strategic accounts, managing the full sales cycle, exceeding quotas, engaging senior safety stakeholders, and collaborating with product, consulting, hosting, and managed services teams. The role requires experience selling complex SaaS, technology, consulting, and managed services solutions to pharmaceutical, biotech, medical device, and clinical research organizations, with strong knowledge of drug safety and pharmacovigilance.

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.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Build Paddle’s data science function and production decisioning systems from the ground up. Prioritize high-value use cases, personally deliver the first ML or agentic system, and own deployment, monitoring, experimentation, reliability, governance, and financial impact. The role will establish the production ML stack, collaborate across Product, Engineering, Risk, Finance, and Compliance, then hire and lead data scientists and ML engineers. Experience with regulated transactional domains, production ML operations, experimentation, and executive stakeholder management is required.