Top Machine Learning Jobs in Italy

The Generative AI Engineer will define strategies for Generative AI, design and build innovative products, and mentor engineers, focusing on AI transformation for financial services clients.

Lead organization-wide software and AI engineering strategy, design scalable cloud-native systems, drive deployment and data integration best practices, and deliver rapid prototype-to-production solutions. Own LLM and ML strategy including model fine-tuning, RAG, AI evaluation, and observability. Mentor technical leaders, shape developer experience and knowledge management, and represent engineering at executive and industry levels.

Freelance, part-time remote role improving AI-generated Italian content through data collection, evaluation, and annotation. Tasks include pairwise comparisons, counting, object tagging and labeling across audio, video, images, and other collected data. Flexible schedule, long-term project, immediate start.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

Join Risk Advanced Technology to apply ML/AI and BI tools to risk problems, develop credit risk and quantitative models, build dashboards and automation, support digital transformation, and present analyses to clients across project phases.

The Process Engineer will develop AI techniques for image analysis, design software for machine vision, and evaluate algorithms in the Photonics Industry.

Lead client AI transformation engagements from discovery through implementation. Develop AI strategies and roadmaps, apply product management practices to prioritize and deliver solutions, collaborate with engineering and data teams, participate in Agile delivery, mentor consultants, support business development, and ensure successful, timely delivery of AI and analytics solutions.

Lead design and development of scalable front-end applications using React and TypeScript for an AI/ML-powered cloud-native SaaS platform. Collaborate with product, UX, backend, and ML teams to integrate APIs and real-time services, drive front-end architecture, optimize performance and accessibility, and contribute to testing and continuous improvement.

Intern will develop and test plant process simulation models (LNG, geothermal, heat pumps), apply ML/AI surrogate and reduced-order models for design and operational optimization, support digital development programs, perform data analysis and reporting, learn commercial simulation software, and collaborate with cross-functional teams while following HS&E standards.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

As an intern in the Data & Analytics team, you will collaborate on projects using data science techniques to solve client challenges, developing prototypes and engaging in discussions to deliver impactful results.

Work on analytics and ML projects for clients across industries: design solutions, build prototypes, implement models (e.g., NLP, credit risk, GenAI chatbots), present findings, and collaborate with teams and clients. Expect international travel, mentoring, and varied project exposure.

Work on client-facing data science and analytics projects: design analytical solutions, build and validate ML/NLP/GenAI prototypes, implement models and toolkits, communicate insights to clients, and support cross-industry engagements (financial services, energy, retail, telecom). Collaborate in teams, travel internationally, and contribute to project delivery and client impact.

Work on client-facing data science and analytics projects: design analytical solutions, build prototypes, implement models (NLP, ML), validate tools (including GenAI), and present findings. Collaborate across teams, travel internationally, and receive mentoring while contributing to real-world financial services, sustainability, and operations use cases.

Work with traders, asset operators, and developers to analyze electricity market outcomes, improve trading and operational strategies, and drive customer success. Translate market workflows into product requirements, collaborate with engineering to enhance analytics, dashboards, and optimization models, and lead customer onboarding, implementation, and ongoing technical account management.

Drive AI adoption by identifying and prioritizing AI use cases, developing business cases, collaborating with data and tech teams to implement solutions, facilitating workshops, enabling stakeholder adoption, and ensuring responsible AI governance and value realization across the enterprise.

Chloris Geospatial seeks experienced individuals with diverse skills in areas such as forest carbon, remote sensing, machine learning, and software engineering, to support their mission of fostering a sustainable economy.

Lead design and adoption of the Operations Target Operating Model, govern and deliver Ops improvement initiatives (including automation and AI), quantify and track cost-to-serve impact, coordinate multi-stakeholder M&A and growth activities, and produce COO-ready strategic outputs. Own KPI frameworks, initiative prioritization, and enablement of automation/AI use cases while coaching associates and ensuring TOM adoption across Operations.

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 product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

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.

Ensure data quality and tracking across GA4 and BI platforms; configure reports and dashboards; analyze on-site behavioral data to generate UX/CRO recommendations; build customer journey maps; partner with marketing, tech and UX to run advanced analyses and attribution tests; support predictive modeling and monitor tracking anomalies; promote data literacy across teams.

The AI Product Specialist collaborates with cross-functional teams to develop, launch, and enhance AI-driven healthcare products, ensuring they address customer needs and market demands.

Collaborate with clients to design and implement data-driven solutions, build ML/NLP/GenAI prototypes, develop credit risk and probability-of-default models, analyze datasets, validate analytical approaches, and communicate results to stakeholders while working across teams.

Lead AI-enabled process transformation engagements: analyze and redesign processes, gather requirements, design and implement AI/use-case solutions, build BI/advanced analytics dashboards, ensure governance and controls, support full project lifecycle, and mentor junior staff in a client-facing consulting role.