Top Machine Learning Jobs in Italy

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 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 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.

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.

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.

Lead analysis, monitoring and management of credit risk to support underwriting decisions. Produce reporting on new production performance, cost of risk and acceptance scoring; update credit policies; monitor portfolio KPIs; support accounting analysis of cost of risk and develop underwriting/scoring procedures.

Lead global marketing strategy and GTM to scale Aisle into a market leader. Own brand, PR, product positioning, full-funnel demand generation, ABM, marketing tech stack, RevOps, attribution, budget/agency management, and grow a multidisciplinary marketing team aligned to revenue targets.

Lead professional services business development by positioning Workday as a platform for business transformation. Advise customers on AI, Extend, and Prism-driven solutions, align with Global SIs, run consultative discovery, negotiate and close services agreements, manage pipeline and forecasting, and build executive-level relationships to drive value-based outcomes.

Lead design and productionization of ML pipelines, evaluation systems, and AI agents for cybersecurity. Own experimentation-to-production lifecycles, integrate ML across product teams, scale ML infrastructure, optimize agent performance, and mentor junior engineers while collaborating with CTO and product leadership.

Lead end-to-end data science projects, design and deploy ML/AI models in production, translate business problems into analytical solutions, ensure code quality and model governance, mentor junior data scientists, collaborate with stakeholders and engineering to drive measurable business impact and promote responsible AI practices.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

The ML Ops Engineer will build and operate scalable ML inference platforms, focusing on model serving infrastructure and deployment pipelines for AI applications.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.