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.

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.

Design, build, test, and improve plugin-based forecasting models from business need through production deployment. Own ML and modeling initiatives end to end, evaluate performance across customer environments, distinguish generalizable solutions from customer-specific requirements, and use AI-assisted engineering tools. Work autonomously with customers and cross-functional stakeholders in a distributed international team, with occasional travel.

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.

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.

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.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.

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.

Design, build, and own scalable backend services, APIs, and high-performance web frontends for an autonomous procurement platform. Improve reliability, security, and performance, implement async task pipelines, and collaborate with product and design. Mentor juniors, participate in code reviews, and leverage AI-assisted tools while shipping iteratively in a globally distributed, async-first team.

Design, deploy, and optimize machine learning and AI solutions, including fine-tuning open-source and commercial LLMs, implementing RAG, and developing generative AI models. Conduct exploratory data analysis and feature engineering, apply traditional machine learning methods, validate solution quality, collaborate with engineers and stakeholders, and maintain technical documentation for reproducibility and knowledge sharing.

Develop complex cloud applications for the Altium 365 platform. Responsibilities include building cloud-native applications and scalable infrastructure, designing APIs and data models, developing stateful and stateless services, working with relational databases and ORM frameworks, and contributing to AI-powered search and large-scale data processing. The role requires technical ownership, collaboration, solving complex challenges, influencing technical direction, and establishing engineering standards and governance.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Design, build, and operate production AI systems including LLM agents, retrieval pipelines, event intelligence, and real-time inference services. Own architecture, orchestration, evaluation, guardrails, observability, reliability, and cost optimization at scale. Partner with platform, product, and research teams, and provide technical mentorship while developing resilient distributed systems for high-volume event streams.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

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

Develop complex cloud applications and scalable infrastructure for the Altium 365 platform. Build distributed, high-load systems and platform features involving APIs, databases, microservices, authentication, caching, messaging, search, and multi-tenancy. Perform performance profiling, optimization, load and stress testing, influence architecture across teams, and establish engineering standards, best practices, and technical governance.

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.

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.