Top Machine Learning Jobs in Lisbon

Own the roadmap and end-to-end delivery of an AI product area across Intermedia’s communication products. Conduct customer and stakeholder discovery, define requirements, prioritize backlogs, partner with engineering and UX, and manage Agile delivery. Use product metrics, experimentation, customer feedback, and market insights to guide decisions and improve usability. Communicate progress and risks while aligning cross-functional teams on product priorities.

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.

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.

Build, deploy, operate, and improve production machine learning systems for pricing, demand forecasting, and fleet revenue management. Responsibilities include ML system architecture, feature serving, model deployment, monitoring, rollback, quality automation, MLOps collaboration, cloud platform usage, and production software engineering. The role partners closely with Data Scientists, Product, and Engineering while establishing standards for coding agents and ML lifecycle execution.

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.

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.

Leads client-facing architecture and delivery of production-grade AI/ML systems, including agentic workflows, multi-agent systems, RAG pipelines, search, recommendations, and decision-support tools. Advises clients, leads and mentors ML engineering teams, manages technical engagements, and oversees projects from discovery and architecture through deployment, monitoring, and iteration.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

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.

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.

Designs, develops, and maintains end-to-end machine learning and data pipelines, ML services, APIs, and production deployments. Implements MLOps practices including CI/CD, testing, monitoring, quality controls, and model lifecycle management. Works with AWS SageMaker, Docker, Kubernetes, and GenAI solutions while troubleshooting production systems and making architectural decisions. Partners with data scientists, mentors team members, and contributes in an Agile/Scrum environment.

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.

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.

You will solve complex problems related to cross-border logistics and B2B systems, focusing on high-load architecture and AI-driven tools.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Own the Subscriptions roadmap end-to-end: research, prioritize, coordinate cross-product dependencies, drive features through delivery, and enable sales and documentation to maximize retention and revenue.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.