Top Machine Learning Jobs in Dublin

Leads global strategic sourcing for MarTech, digital media, AdTech, data analytics, and market research portfolios. Manages sourcing professionals, develops category strategies, negotiates SaaS and technology contracts, oversees partner relationships, benchmarks commercial performance, and modernizes procurement processes. Partners with executive Marketing, IT, Data, Finance, Legal, and InfoSec stakeholders to integrate AI, optimize commercial models, mitigate risks, and improve operational efficiency. Occasional travel may be required.

Principal Data Scientist leading advanced data science, machine learning, Generative AI, predictive modeling, and cloud migration initiatives for insurance products. Responsibilities include building production data pipelines and ML workflows, developing predictive models, deploying data products, supporting Azure and Databricks infrastructure, ensuring model governance, and collaborating with product, technology, and data engineering teams. The role also requires technical leadership, stakeholder communication, mentoring, innovation, and translating complex analytical results into business value.

Leads global privacy program operations, governance, and risk mitigation across enterprise technologies and data platforms. Embeds privacy-by-design controls into data lakes, warehouses, analytics, AI, and GenAI environments; audits control effectiveness; develops policies, training, dashboards, and metrics; and translates privacy laws and regulatory requirements into scalable technical solutions. Partners with legal, technology, data governance, developers, architects, and global business units on privacy engineering, responsible AI, compliance, and cross-functional initiatives.

Research, develop, deploy, and improve machine learning and LLM-based classification systems for cross-border trade. Responsibilities include exploratory data analysis, NLP and transformer modeling, production optimization, model evaluation, stakeholder consultation, documentation, and knowledge sharing.

Build scalable, low-latency backend APIs, event-driven services, integration layers, and microservices supporting personalization and recommendation experiences. Partner with AI/ML and data engineers to deploy models, expose AI services, and develop React applications. Improve security, observability, reliability, performance, scalability, and operational efficiency while using AI-assisted engineering tools to enhance delivery and code quality.

Curate, characterise, and validate preclinical and translational pathology datasets and AI model outputs. The role involves statistical analysis, quality control, confounder and batch-effect detection, reproducible pipelines, benchmarking, interoperability, metadata standards, and evidence packages for computational pathology applications. The scientist will collaborate with pathologists, AI, software, product, and external consortium teams while supporting BIGPICTURE and VICT3R project delivery under traceability, GDPR, and GLP-aligned practices.

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.

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.

Build BLP’s core software across infrastructure, backend, machine learning, and product extension platforms. Responsibilities include operating secure and observable cloud infrastructure, developing performant distributed systems, applying AI to business problems, and enabling customer process automation. The role emphasizes formal reasoning, correctness by construction, maintainable systems, type systems, and functional programming. Work is available onsite in Zurich or remotely within Europe.

Develop and ship production machine learning and statistical algorithms for MongoDB’s core server, query engine, and Atlas platform. The role focuses on resource scaling, release safety, monitoring, automated incident response, and query performance. Responsibilities include prototyping, testing, deployment, model feedback loops, metrics and dashboards, code reviews, and collaboration with engineering teams to deliver measurable business impact.

Lead creation of an AI-first engineering capability for database products: design reusable frameworks, ensure secure production code, guide AI-enabled delivery, set governance and guardrails, influence senior stakeholders, and mentor engineers to scale cross-product adoption.

Drive revenue growth for Salesforce Agentforce by identifying, qualifying, and pursuing enterprise B2B opportunities. Conduct technical discovery, map customer needs to Salesforce capabilities, present value-focused points of view, and explain AI solutions to technical and business stakeholders. Build client relationships, collaborate with Salesforce teams, demonstrate complex use cases, and consistently exceed sales targets.

Design and lead scalable backend services and APIs using Java, Spring Boot, and cloud technologies. Provide architectural direction, mentor engineers, drive complex technical initiatives, and collaborate with cross-functional teams and customers. Research and integrate new technologies, including AI and machine learning capabilities, while contributing across multiple products and technical stacks in a flexible hybrid environment.

Lead end-to-end implementation and onboarding of HiredScore's AI-powered SaaS platform for global enterprise customers. Manage project governance, planning, stakeholder reporting, scope, quality, timelines, and issue resolution. Translate business requirements into product configurations, create onboarding materials, and coordinate with Product, Data Science, Customer Success, and Support to ensure successful deployment and transition to long-term adoption.

Design, build, and deploy scalable AI and generative AI products from prototypes through production. Collaborate within an agile, multidisciplinary team while applying software engineering principles and object-oriented programming.

Drive Agentforce adoption across Poland and EMEA Emerging Growth Markets through enterprise B2B sales. Identify and qualify opportunities, lead technical discovery, present business-value propositions, explain Salesforce solutions, and partner with sales and cross-functional teams. The role requires translating complex AI and Salesforce capabilities into outcomes for technical and business stakeholders while exceeding sales targets and building strategic customer relationships.

Supervise portfolio accounting reconciliation services, including cash, position, trade, and OTC reconciliations. Review break items, controls, procedures, and operational deliverables while resolving issues and escalating aged items. Lead and mentor portfolio accountants, set goals, monitor performance, coordinate with internal teams, and ensure deadlines are consistently met.

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.

Machine Learning Interns will own a scoped ML project, from problem definition and experimentation through implementation and results communication. They will analyze data, develop and evaluate models, contribute to applied AI research, write tested and maintainable code, and apply techniques such as NLP and graph-based modeling to ranking, search, discovery, and advertising problems. The role involves collaboration with engineering, product, and data teams in a hybrid Dublin office setting.

Lead data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

Conduct original research applying machine learning, deep learning, and NLP to extract patterns from large and unstructured datasets; implement and back-test algorithms in high-quality code; document findings and collaborate with trading teams to deploy research-driven models.

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.

Perform malware and threat research across the attack lifecycle, validate and improve Falcon detections, collaborate with engineering and ML teams, automate analysis, support external security testing, and translate findings into product protections.