Top Machine Learning Jobs in Dublin

Own the vision, strategy, roadmap, and delivery for Microblink’s Platform and Know Your Agent products. Lead and develop product managers, partner with engineering, design, sales, marketing, and customer success, and manage product metrics. Serve as the VP of Product’s second in command by running planning, prioritization, roadmap reviews, cross-functional alignment, product operations, escalations, and leadership communications.

Drive Azure and AI sales across the DACH market, managing opportunities from qualification through close and building pipeline against Azure consumption, workload adoption, and AI deployment targets. Lead customer conversations, technical sales cycles, workshops, proofs of concept, and solution envisioning. Translate customer challenges into secure Azure architectures, guide responsible AI adoption, and build trusted relationships with senior decision makers while collaborating with account executives, engineers, partners, and customer success teams.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Designs, integrates, validates, and improves robotics and automation systems, equipment, and tooling in regulated manufacturing environments. Leads complex engineering tasks, develops test and maintenance plans, investigates root causes, implements reliability improvements, maintains configuration control, and coordinates cross-functional deployment readiness. The role also requires PLC/HMI troubleshooting, network technology knowledge, vision systems, machine learning, API integration, documentation, compliance, and mentoring junior engineers.

Supports the strategy, development, launch, scaling, and continuous improvement of AI-powered products. Translates product vision into roadmaps, manages product lifecycles, prioritizes features, tracks budgets and resources, and evaluates technical, adoption, and value metrics. The role coordinates cross-functional teams, business owners, DDIT, and external partners while ensuring stakeholder alignment, user satisfaction, ethical AI deployment, and successful transition to maintenance.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.

Leads global AI engineering teams delivering secure, scalable AI capabilities from experimentation through production. Owns engineering strategy, architecture, portfolio planning, MLOps and LLMOps standards, production reliability, responsible AI, customer engagements, and talent development. Provides hands-on technical leadership across machine learning, generative AI, RAG, agentic systems, cloud, edge, and enterprise software while aligning delivery with customer value and business priorities.

Leads AML transaction-monitoring optimization, rule testing, threshold tuning, scenario design, and false-positive reduction. Drives FIU automation and workflow improvements, evaluates AI and LLM tools, supports alert triage and investigations, and partners with Product, Engineering, Operations, and vendors on compliance technology integrations. Helps scale monitoring frameworks across products, jurisdictions, and transaction volumes while maintaining regulatory alignment.

Lead and develop machine learning, LLM, and automation projects using large-scale network and device telemetry. Clean and transform data, engineer features, build models for anomaly detection, forecasting, and optimization, and deliver reproducible analyses with Python and SQL. Collaborate with engineering and product teams to deploy, integrate, and monitor models in production while contributing to MLOps workflows.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

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.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

Design, build, and optimize a multi-region, highly-available Java/Scala microservices data pipeline processing thousands of messages/sec. Improve performance, implement fraud-detection pipelines with ML (Kafka/Kafka Streams), deploy to multi-cloud Kubernetes, and push frequent production releases to maintain five-nines uptime.

Drive new commercial customer acquisition across the UK and Ireland by building outbound pipeline, managing complex sales cycles, advising executive stakeholders, negotiating and closing deals, and expanding existing accounts. The role requires consultative value selling of Zendesk’s AI-powered platform, collaboration with cross-functional teams, and strategic use of sales and customer data.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

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 the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Lead customer-facing Zendesk implementation projects from discovery and solution design through configuration, onboarding, training, and adoption. Translate business requirements into scalable platform solutions, manage project scope, timelines, budgets, risks, and competing priorities, and communicate recommendations to technical and executive stakeholders. Collaborate with services, sales, product, and post-sales teams while maintaining product expertise across Zendesk Support, Messaging, Knowledge, Voice, Analytics, QA, Workforce Management, Copilot, and AI agents.

Leads transformational, cross-organizational risk initiatives at Stripe, shaping product and technical strategy across infrastructure, data, AI, APIs, and user experiences. Partners with engineering, data science, ML, and senior leadership to drive execution, adoption, investment decisions, and measurable outcomes. Builds alignment across teams, mentors product managers, and advances product management practices. Focus areas include fraud, credit, payments, financial risk, and other high-stakes decision systems.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.