Top Machine Learning Jobs in Dublin

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

Lead global finance systems strategy and roadmap (NetSuite, BlackLine, FloQast, Pontus), drive AI and automation in finance, govern integrations and SOX controls, partner with finance and engineering stakeholders, recruit and develop product and systems teams, and deliver outcomes like faster closes, reduced manual effort, and improved data integrity.

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

Design, build, and manage large-scale search and content recommendation systems using machine learning and personalization techniques. Collaborate with content, product, and business teams to define success metrics, drive AI product improvements, and maximize content value and user impact.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

Lead development and productionization of TTS and voice-generation systems: train, fine-tune, evaluate models, deploy and optimize for inference, latency, scaling, monitoring, and reliability; translate research into production and define technical direction while collaborating with research, engineering, and product.

Lead the estimating product roadmap for an AI-powered construction SaaS: prioritize and ship MVPs, run rapid experiments, conduct user discovery with estimators, define success metrics, and iterate using data and user feedback.

Lead and grow multiple small, high-output engineering teams focused on AI-powered vulnerability management. Stay hands-on with architecture and code reviews, drive hiring and career development, ensure technical excellence, and coordinate cross-team priorities to deliver customer and revenue impact.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

The role involves designing, building, and integrating ML solutions, optimizing generative AI products, and ensuring high-quality output for enterprise customers.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

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.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Serve as strategic Customer Success Manager for enterprise clients, driving ROI from fraud detection and AML platform use. Monitor detection performance, advise on ML and rule-engine tuning, coordinate with Product and Engineering, run business reviews, educate clients on best practices, and translate customer needs into product feedback to reduce fraud and streamline operations.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Manage and mentor a team of ML scientists, driving collaboration with engineering and product leads to deliver impactful ML products while enhancing team productivity.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Own post-sale customer lifecycle for Portuguese utility accounts: onboard, embed Neara into workflows (vegetation management, clearance analysis, CAPEX), run QBRs, monitor health scores, drive renewals and upsell, and align product roadmap with local market needs.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

Design, develop, and implement machine learning models and algorithms, collaborate with teams to enhance AI/ML services, and ensure data quality.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Build and operate end-to-end ML infrastructure for training, evaluation, deployment, inference, observability, and CI for large-scale AI systems. Optimize reliability, latency, throughput, cost, and developer tooling to enable rapid experimentation and production-grade model serving.

Design, prototype, and deploy ML models for personalization in mobile games. Implement and evaluate algorithms (including RL and generative methods), run A/B tests, convert prototypes to production via MLOps, monitor player data, and collaborate with product, engineering, and analytics teams to improve retention and monetization.