Top Tech Jobs & Startup Jobs in Dublin

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.

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 applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Design, implement, and operate the Virtual Private Cloud control and data planes. Improve performance, reliability, automation, and cross-service interactions. Develop networking features, perform load testing, and write high-performance code for cloud networking services.

Build and operate services and tooling that automate network lifecycle, make network changes safe (CI/CD, rollouts, audit trails), develop scalable observability and telemetry, integrate network source-of-truth with platform APIs, and collaborate with network engineers and SREs to turn operational pain into production-grade automation.

Design, deploy, and optimize global enterprise wired/wireless networks and VPNs. Lead connectivity between branches, automate operations and monitoring, troubleshoot cross-region incidents, evaluate new technologies, and partner with telecom providers to manage SLAs and escalations.

Lead hands-on applied AI work: prototype demos, accelerate customer POC-to-production, perform technical onboarding, optimize distributed training and inference on GPU infrastructure, research and implement emerging ML techniques, and feed actionable product feedback while producing reusable assets and benchmarks.

Lead reliability and observability for compute nodes running VMs. Debug Linux user/kernel issues, troubleshoot CPU/memory/NUMA/cgroups, operate QEMU/KVM and container tech, design node-level metrics/logs/traces/SLIs/SLOs, run incident response and collaborate across platform, kernel, GPU and infrastructure teams.

Work on YDB distributed storage and database systems to maximize performance on modern and legacy hardware (NVMe, HDD, DPUs). Reengineer components for low-latency, high-throughput workloads, profile and debug production systems, and design resilient distributed storage primitives. Collaborate on incident resolution and production deployments to support Nebius cloud 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.