Top Machine Learning Jobs in Dublin

The Senior Applied AI/ML Scientist will build and ship LLM-powered features, drive quality in evaluation, improve retrieval systems, and lead technical discussions with a focus on AI and engineering collaboration.

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.

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.

Embed with engineering clients to own full-cycle technical hiring for software, AI/ML, infrastructure and data roles. Run deep intakes, headhunt passive candidates, coach hiring managers on interview design and calibration, build hiring foundations, report on pipeline health, and use/builder recruitment AI agents to scale impact.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Leads development and deployment of expert and generalist robot policies for industrial automation. Designs task-planning and policy-learning systems using hierarchical planning, reinforcement learning, and hybrid methods. Develops simulation and real-world datasets, guides sensor integration, collaborates across embedded and hardware teams, and advances AI platform architecture, CI/CD, and MLOps. Provides technical leadership, establishes roadmaps, mentors engineers, and deploys robotics algorithms into real-world systems.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

The Executive Director leads HEOR Consulting projects, manages teams, ensures client satisfaction, and drives AI integration within healthcare solutions while overseeing business development and strategic planning.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Lead and mentor a team of Applied Scientists and AI/ML Engineers to design, build, and deploy production-grade AI features for an AI-native CRM. Drive technical strategy for LLM operations, agentic workflows, RAG/vector-based solutions, and scalable data pipelines while partnering with platform teams to ensure performant, customer-centric ML systems.

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.

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.

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 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.

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.

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

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.

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.

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.