Top Machine Learning Jobs in Dublin

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.

Lead design and deployment of advanced signal-processing and machine-learning algorithms for power system monitoring and grid analytics. Architect Edge-to-Cloud solutions for embedded, resource-constrained systems, guide end-to-end development from modeling through production, influence product strategy, collaborate with cross-functional teams and external partners, and mentor engineers toward scalable grid intelligence products.

Lead testing strategy for AI-powered legal products: build evaluation frameworks, design test plans, measure AI outputs, run regression and UAT, identify risks, and partner with engineering to improve quality and scale the testing function.

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.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production optimization. Build ML infrastructure for payment routing, establish reliable MLOps practices, partner with product, data, engineering, and customers, mentor engineers, and guide technical priorities. Apply statistical thinking, A/B testing, pragmatic model evaluation, and reinforcement learning concepts in a remote-first environment.

Own an AI investing product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering and cross-functional teams to build reliable, auditable financial products involving payments, regulatory compliance, financial rails, and third-party dependencies. Apply AI with appropriate evaluation, safeguards, human oversight, and failure handling while balancing correctness, reliability, cost, speed, security, and user experience.

The Senior Product Owner will lead the product roadmap for a data analytics solution, collaborating with various stakeholders to ensure strategic direction, market analysis, and customer engagement, while managing vendor relationships and delivery planning.

The Engineering Manager will lead the AI Platform Team, overseeing infrastructure development, hands-on coding, and supporting ML teams while planning high-impact roadmaps and improving developer experience.

The Senior Machine Learning Scientist will identify ML opportunities, develop algorithms, analyze data, and collaborate to enhance customer experiences with AI solutions.

The Staff Machine Learning Scientist will define ML features, mentor engineers, research algorithms, and develop prototypes, focusing on customer value.

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.

Leads business oversight, User Acceptance Testing, quality assurance, release validation, and testing framework development for business-critical applications supporting NAV and Middle Office operations. Partners with Product Development, Client Services, and global stakeholders to define test strategies, acceptance criteria, and validation processes. Identifies and manages defects and risks, supports business sign-off, promotes automation and continuous improvement, and serves as a subject matter expert for operational and testing matters.

Design, deploy, and operate production ML models and MLOps workflows (real-time and batch). Partner with ML scientists and studios to integrate ML services into games, build CI/CD, model registry/feature store practices, monitor drift, prototype ML/GenAI solutions, and mentor engineering teams.

Build and productionize generative AI and classical ML solutions for healthcare: data preparation, feature engineering, model/prompt development, evaluation, deployment, MLOps, monitoring, retrieval/indexing, and responsible AI practices while collaborating with cross-functional stakeholders.

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.

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.

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

As an AI Product Manager, you will define product roadmaps, collaborate with cross-functional teams, and optimize AI solutions to meet user and business needs.

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.

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.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

The Fund Accounting Supervisor oversees NAV calculations, reconciliations, and guides a team of Fund Accountants while ensuring timely delivery and compliance.