Top Machine Learning Jobs in Dublin

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.

Lead the design, development, deployment, and monitoring of scalable multimodal AI and machine learning systems across computer vision, audio, and NLP. Own the complete ML lifecycle, build training pipelines and inference APIs, productionize models with cloud and MLOps infrastructure, implement retraining workflows, and apply emerging techniques such as foundation models, generative AI, and self-supervised learning. Collaborate cross-functionally with engineering, product, data, and design teams to deliver business impact.

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.

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.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

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

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

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

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Design, deploy, and maintain production-grade distributed ML systems for revenue management. Build data pipelines, implement and validate models, establish MLOps practices (CI/CD, orchestration, monitoring), define SLIs/SLOs, and collaborate with product and engineering to drive model reliability and business impact.

Own and ship core ML subsystems end-to-end for a proactive, long-horizon AI assistant. Work includes data preparation, training, evaluation, inference, debugging production failures, optimizing for latency/cost/reliability, and mentoring other ML engineers.

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.

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

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

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.

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.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design and implement AI/ML features for conversation intelligence: build end-to-end pipelines from data ingestion and feature engineering to model development, validation, deployment, and monitoring. Instrument services with metrics and telemetry, participate in on-call rotations and progressive rollouts, and collaborate across teams to review code and improve production inference reliability and performance.

Lead a small senior data engineering team owning the end-to-end data platform: streaming, warehousing, modelling, pipelines, ML lift, and operational excellence. Drive delivery, coach engineers, review technical designs, enforce data contracts and quality, manage costs (FinOps), and partner with Product and leadership to set roadmaps and risk. Build and grow an emerging ML function.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.