Top Machine Learning Jobs in Lisbon

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.

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.

Leads strategic R&D engagements from presales through delivery, translating scientific and business challenges into AI architectures, product backlogs, MVPs, simulations, integrations, data pipelines, and production solutions. Builds trusted relationships with R&D executives, scientists, engineers, and technical teams; assesses data, compute, feasibility, value, staffing, and delivery plans. Develops reusable architectures and playbooks, oversees technical workstreams, evaluates emerging AI capabilities, and provides feedback to shape Microsoft Discovery products and scalable industry solutions.

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.

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.

Build and deploy production-ready AI pipelines integrating LLMs and foundation models. Develop scalable GenAI services, transition prototypes into production, implement LLMOps and monitoring, optimize RAG pipelines and vector database retrieval, and explore prompt engineering, fine-tuning, and agentic workflows. Collaborate with data engineers and business stakeholders to deliver AI solutions and communicate technical concepts to nontechnical teams.

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.

Perform multilingual legal data collection, evaluation, and annotation for international business contracts. Analyze clauses, obligations, and terms; participate in onboarding, calibration sessions, and quality reviews; and collaborate within secure client environments to support AI training and evaluation for contract management platforms. The role requires legal or contract expertise, English and Portuguese fluency, and strong contract interpretation skills. Data annotation or machine learning evaluation experience is preferred.

Review and annotate images by identifying people, actions, and predefined categories. Follow detailed annotation guidelines, maintain accuracy and consistency, review completed work, and help create high-quality datasets for AI models. The role requires strong attention to detail, independent work, reliability, and EU citizenship with residence in the European Union.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Develop and maintain high-load, real-time personalization services for global e-commerce platforms. Improve shopper experiences and business KPIs through applied machine learning, user interaction modeling, personalization and recommender systems, hypothesis validation, and A/B testing. Collaborate with engineering teams and business stakeholders to research, deliver, and communicate personalization capabilities and outcomes.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

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.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.