AI Jobs in Dublin

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As an AI Infrastructure Engineer at Together, the responsibilities include participating in on-call rotation to respond to production incidents, building and running infrastructure using Ansible, Terraform, and Kubernetes to support scaling to a large number of concurrent users, building monitoring systems to ensure high-quality service, designing and implementing operational processes such as deployments and upgrades, debugging production issues across all services and stack levels, identifying improvements for product architecture in terms of reliability, performance, and availability, and planning the growth of Together AI's infrastructure.

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Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.

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Utilize proprietary software to provide accurate input and labels for healthcare and administration projects, ensuring high-quality data for AI model training. Deliver curated, high-quality data for scenarios involving patient care coordination, medical billing, administrative workflows, and healthcare operations. Collaborate with technical staff to support the training of new AI tasks and contribute to the development of innovative technologies. Assist in designing and improving efficient annotation tools tailored for healthcare and administration data. Select and analyze complex problems in healthcare and administration fields aligned with your expertise to enhance AI model performance. Interpret, analyze, and execute tasks based on evolving instructions, maintaining precision and adaptability.

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The Senior+ Solutions Engineer at Crusoe Cloud is responsible for leading technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers, owning the proof of concept (PoC) process through to post-sales optimization. They architect and deploy ML workloads using Kubernetes-based stacks such as Ray and Kubeflow, design infrastructure focusing on performance, scalability, and efficiency, and deploy and optimize AI/ML workloads directly on Crusoe infrastructure ensuring performance at the container and hardware level. They assist customers in migrating and adapting workloads across AWS, Azure, and GCP, and explain tradeoffs between cloud-native and Crusoe-native approaches. Additionally, they conduct workshops, live demos, and solution reviews, contribute to case studies, solution briefs, and blog posts highlighting customer success, and act as a voice of the customer by relaying feedback to internal engineering and product teams to improve Crusoe’s platform based on real-world implementation experience.

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Build systems that integrate seamlessly with clinical workflows, particularly with Electronic Health Records (EHRs) used in healthcare, focusing on making the AI feel native rather than a plugin. Develop systems that simplify the complexities of healthcare operational rules, compliance, and funding constraints so that clinicians do not have to deal with them directly. Write clean, testable code with strong interfaces, error handling, and observability to ensure trust and quality. Take ownership of outcomes to ensure the developed systems help clinicians and improve practice revenue, not just function technically. Build AI-assisted workflow functionality for clinical documentation tasks such as extraction, reconciliation, and drafting with human review and auditability. Work collaboratively with others in a shared design and implementation environment, including pairing. Learn the healthcare domain, particularly UK and US healthcare operational requirements and constraints, to improve products accordingly.

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Build systems that integrate Heidi into clinical workflows and EHRs used in American healthcare, making Heidi a native capability rather than a plugin. Develop AI systems that manage the complexity of healthcare billing, compliance, and payer constraints to simplify the experiences for clinicians. Write clean, testable code with strong interfaces, thoughtful error handling, and observability to ensure trust and quality in workflows relied upon by clinicians, operators, and downstream systems. Take ownership of outcomes to ensure developed systems effectively help clinicians and improve practice revenue. Develop AI-assisted functionalities for extraction, reconciliation, and drafting across workflows with human review, auditability, and clear controls. Collaborate closely in a highly interactive environment, including frequent pairing and shared ownership of design and implementation. Continuously learn about healthcare organizations' operations and regulatory requirements, especially for US customers, and use this knowledge to inform product improvements.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Conceptualize scripts based on current production needs and existing AI characters. Create narrative-driven, longform video content, including stylized and explicit NSFW visuals, focusing on storytelling, atmosphere, and visual coherence. Own and manage end-to-end AI video production workflows from ideation, prompting, generation, editing, to post-production. Work extensively with ComfyUI pipelines by building, customizing, and optimizing node-based workflows for image and video generation. Utilize tools like Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, and other AI video platforms to produce high-quality visuals. Develop and maintain consistent character appearance, style, and scene continuity across longer narratives. Integrate motion graphic design and colour correction for cohesive final outputs. Experiment rapidly with new AI models, tools, and techniques, incorporating them into workflows and sharing skills with the team. Align with the Content Lead's creative direction while maintaining autonomy in execution and technical decisions. Continuously refine workflows for efficiency, scalability, and output quality.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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The Staff Applied AI Researcher at Articul8 AI is responsible for setting the technical direction for agentic reasoning systems and runtime intelligence across ModelMesh™, including defining orchestration strategies, decision policies, verification approaches, and runtime quality standards for parallel agent systems in production. They must architect infrastructure for large-scale researcher augmentation, design agentic platforms and orchestration primitives to enable extensive deployment of AI agents in experimentation, evaluation, and production integration. The role requires advancing the science of autonomous reasoning by designing, training, and refining learned components for runtime decisioning through widespread agent-driven experiment pipelines. The researcher must unify perception, retrieval, reasoning, and action by developing methodologies for integrating domain-specific models, data perception systems, knowledge graphs, retrieval layers, and external tools into cohesive agentic workflows. They lead research on agent reliability in regulated environments by driving failure detection, verification workflows, error analysis, and auditable autonomous behavior research using agent-orchestrated stress testing and red-teaming. The position entails defining evaluation methodologies for runtime intelligence to measure task success, decision quality, robustness, traceability, and failure recovery under enterprise conditions, building continuous agentic evaluation harnesses. Additionally, the researcher influences platform architecture decisions related to model routing, tool use, observability, governance, access control, and interoperability with external agent ecosystems. Mentoring researchers in the agentic paradigm, contributing to hiring, and maintaining a hands-on personal research impact through technical work, publications, patents, and visible output are also core responsibilities.