Top Machine Learning Jobs in Italy

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.

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.

Corti is seeking passionate individuals to join their talent community, shaping the future of healthcare through AI technologies. Opportunities include roles in AI technology, machine learning, and software development, focusing on reducing errors and improving patient outcomes.

Supports customer-facing AI initiatives by helping discover use cases, build prototypes, configure virtual agents and conversational workflows, test solutions, and create technical demonstrations and documentation. The role collaborates with senior architects, sales, product, and engineering teams while developing expertise in enterprise AI, LLMs, RAG, APIs, cloud platforms, and customer experience technologies. It also involves customer workshops, technical presentations, sandbox engagements, and internal enablement.

Optimize large-scale ML inference systems for throughput, latency, cold-start time, and cost per token. Tune SGLang, vLLM, and TensorRT-LLM across GPUs and distributed clusters; improve batching, scheduling, KV caching, quantization, weight loading, JIT caching, and multi-node inference. Profile and instrument systems to validate performance gains, eliminate storage and networking bottlenecks, and establish technical direction through reproducible benchmarks and experiments.

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.

Develop AI-augmented blockchain risk and threat intelligence systems, including autonomous monitoring and investigation workflows. Lead forensic investigations into crypto theft, fraud, and market manipulation. Research post-quantum cryptography and quantum threats to blockchain security, evaluate migration paths, and collaborate with internal teams, external stakeholders, and academic institutions to deploy emerging security tools.

Supports Account Executives throughout the sales cycle by leading technical discovery, solution design, demonstrations, workshops, proofs of concept, evaluations, and technical validation. Translates engineering, simulation, HPC, AI, and infrastructure requirements into Rescale recommendations, documents opportunities in Salesforce, resolves technical risks, supports negotiations, and ensures effective post-sale handoffs across Northern Europe.

Perform multilingual legal data collection, evaluation, and annotation for AI-powered contract management platforms. Analyze clauses, obligations, and terms in international business contracts; participate in onboarding, calibration, and quality reviews; and collaborate in secure client environments. Requires contract or legal expertise, strong legal interpretation and contract-review skills, and fluency in English and Italian. Data annotation or machine-learning evaluation experience is preferred.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Own enterprise customer relationships from kickoff through adoption and ongoing value realization. Lead success plans, QBRs, executive conversations, escalations, stakeholder alignment, training, and strategic recommendations. Translate technical capabilities and performance data into business outcomes such as revenue, conversion, and customer experience. Partner with Account Management to support retention and expansion while improving scalable onboarding and support processes.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

Conduct deep learning research for multimodal foundation models across language, Earth observation, and cardiology. Design neural architectures, implement and evaluate experiments using large-scale GPU and HPC resources, benchmark state-of-the-art methods, guide junior researchers, coordinate research partners, and organize publications and open-source releases.

Develop data-driven solutions for enterprise customers and internal teams, including exploratory analyses, statistical models, machine learning, forecasting, experiments, dashboards, and data products. Translate ambiguous business challenges into measurable analytical problems, collaborate with engineering and product teams on deployment, communicate findings to technical and non-technical stakeholders, and measure business impact across translation quality, operations, customer behavior, marketing, and financial performance.

Develop analytical and machine learning solutions for consulting clients across industries. Responsibilities include designing solutions, building prototypes, evaluating model effectiveness, explaining data science concepts to clients, and collaborating with colleagues globally. Projects involve NLP, credit risk modeling, portfolio decarbonization, promotion analytics, GenAI validation, supply chain risk, and customer churn modeling.

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.

Supports consulting clients by designing analytical solutions, developing machine learning and statistical models, building prototypes, validating AI tools, and communicating complex data science concepts. Projects span financial services, risk management, sustainability, retail, supply chain, and customer analytics. The role involves close collaboration with colleagues and clients, continuous technical learning, and occasional international travel.

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.

Support Oliver Wyman’s Data & Analytics consulting team by designing analytical solutions, building prototypes, evaluating model effectiveness, and communicating findings to clients and colleagues. Projects may involve NLP, machine learning, GenAI validation, credit risk modeling, portfolio decarbonization, promotion analytics, supply chain risk, and customer churn. The role offers international consulting exposure, coaching, and collaboration across industries.

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.

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.

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 a Fraud Data Analyst, you will design real-time fraud strategies, develop rules to combat fraud, optimize decision-making, and collaborate with teams to enhance user experience and report performance metrics.