Top Machine Learning Jobs in Lisbon

Design, develop, optimize, and maintain scalable data pipelines and ingestion solutions for large-scale datasets. Ensure data quality, reliability, and performance while collaborating with Data Science and Engineering teams to deploy and operationalize machine learning models in production. The role also contributes to software engineering best practices, testing, CI/CD, monitoring, and continuous improvement.

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.

Manages and mentors Data Scientists while overseeing the full lifecycle of machine learning models powering real-time payment decisions. Defines the Payment Intelligence roadmap, translates complex data challenges into business goals, and partners with engineering, product, and design teams to deliver production solutions. Communicates ML strategy and technical trade-offs to senior leadership. The role requires hands-on expertise in Python, SQL, cloud-based ML, and complex model architectures, with payments or fraud prevention experience preferred.

Build Solidgate’s ML/AI function from the ground up, developing real-time payment routing models and production pipelines. Own the full ML lifecycle from experimentation and A/B testing through deployment, while solving modeling challenges involving large-scale transactional data. Partner with Product, Engineering, and Data Platform teams to deliver measurable improvements in conversion, approval rates, losses, and revenue.

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 complex, global REFS technology programs from strategy and roadmap development through deployment, adoption, operational handover, and benefits realization. Oversees governance, budgets, vendors, risks, dependencies, stakeholder alignment, change management, and executive reporting. Ensures architecture, cybersecurity, privacy, regulatory, and service-management readiness. Identifies and delivers AI, automation, and robotics initiatives across facilities, workplace, security, capital projects, and building operations.

Leads a cross-functional engineering team building payment intelligence products using machine learning and AI. Owns product delivery, quality, operations, technical standards, prioritization, experimentation, and stakeholder expectations. Manages engineers, a designer, and data scientists while hiring, coaching, and developing team members. Requires hands-on coding experience with Python, Ruby on Rails, JavaScript, and React, plus strong communication, collaboration, and ownership.

Own the Mapping & Content product domain end to end, including strategy, prioritization, economics, and outcomes. Lead efforts to match hotel rooms and content across suppliers, languages, and formats with high accuracy and coverage. Partner closely with engineering and data science teams on entity resolution, search, matching, catalog, identity, recommendation, and content-quality problems. Identify high-value failures, make trade-offs between precision and coverage, and help build Nuitee’s product organization in a fast-moving environment.

Lead research on interactive avatar models and real-time generative AI video agents. Set research direction, advance multimodal perception and reaction generation, post-train diffusion models, build evaluation frameworks, define datasets, and guide research-to-production efforts. Mentor researchers and influence technical decisions across research, data, and engineering teams.

Develop and operate complex, cloud-native applications for the Altium 365 Platform. Responsibilities include building scalable infrastructure, distributed and high-load systems, APIs, data models, services, and platform frameworks. The role involves performance optimization, testing, architectural decision-making, technical governance, and influencing engineering standards across multiple teams. Engineers may also work with AI/ML-powered search, authentication, access management, databases, caching, and message brokers.

Own the global B2B content strategy for enterprise audiences, creating website copy, advertising, whitepapers, case studies, thought leadership, social content, video scripts, and sales enablement materials. Build AI-assisted content production systems using prompt libraries, context engineering, source-of-truth repositories, automated workflows, and human review gates. Partner with Product, Sales, and regional Marketing teams to develop enterprise messaging and improve content quality and performance.

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.

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.

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.

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.

Lead a multidisciplinary team of software engineers, data scientists, and machine learning engineers building personalization systems for search, recommendations, and customer experiences. Own the machine learning roadmap, provide technical oversight, guide end-to-end model development and productionization, and ensure system quality through testing and monitoring. Partner with senior leadership and product stakeholders, manage delivery priorities, and support team development and continuous technical innovation.

Lead Newton’s transition from proprietary APIs to self-hosted and open-weight models. Own production data pipelines, evaluation systems, model comparison, routing, post-training, serving, and rollout. Train models using SFT, LoRA/QLoRA, DPO, distillation, and potentially reinforcement learning. Optimize quality, latency, cost, and data privacy while working independently across product and engineering.

Designs and delivers production credit analytics and machine learning platforms for regulated financial-services clients. Responsibilities include architecture, Python and SQL development, reproducible ML workflows, MLOps, data quality, model governance, Snowflake application reliability, monitoring, access control, and technical leadership. The role works directly with clients and modelling specialists to productionize credit-risk models, support multiple portfolios, and evaluate responsible uses of LLM-assisted analytics.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Build and operate production machine learning systems for global document classification and extraction. Design training, evaluation, labeling, and deployment pipelines; optimize GPU inference for real-time performance; and integrate VLM/LLM and traditional ML capabilities into identity verification products. Lead technical designs, improve scalability and reliability, guide releases, review code, mentor engineers, and coordinate cross-functional solutions.

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.