Top Machine Learning Jobs in Dublin

The role involves improving machine learning ranking models, implementing algorithms, and taking full ownership of machine learning systems within Quora's product.

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.

Lead end-to-end ML systems for LLM/NLP document intelligence and supply-chain predictive analytics. Design and fine-tune transformer-based models, implement RAG pipelines, build forecasting, classification and optimization algorithms, and productionize models using containerization and MLOps tools. Ensure robust, explainable models deployed to secure on-premise environments with monitoring and continuous improvement.

Develop and implement machine learning models to improve user targeting and personalization, analyze data, collaborate with teams, and conduct A/B tests to optimize growth initiatives.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

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.

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Build and own production ML and LLM systems for onboarding, risk, compliance, and operations. Design models, engineer features from messy data, balance performance and interpretability, integrate with backend training/inference pipelines, write tested code, and partner with product and operations to drive scalable, reliable automation.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Own end-to-end ML projects focused on recommendation and LTV signals: develop, evaluate, and deploy models; build training/scoring pipelines; run A/B tests; monitor production models; collaborate with product and analytics; mentor colleagues.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

The Senior Machine Learning Engineer will design, train, and deploy ML models for optimizing revenue in advertising, taking ownership of product development and leading the ML discipline.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

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.

The Staff Machine Learning Scientist will lead ML projects, mentor engineers, define new features, conduct research, and collaborate with product teams to deliver impactful ML products.