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.

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.

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 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 and drive cross-team technical projects across engineering domains (incident, capacity, runtime infra). Align stakeholders including C-level, facilitate technical decisions, establish scalable processes, and collaborate with engineering, infrastructure, and business teams.

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 performance analysis and optimization of large-scale GPU/HPC clusters across hardware and software stacks. Troubleshoot real workloads, qualify and integrate new hardware, tune system configurations (networking, virtualization, kernel), support escalations, and collaborate with infrastructure teams and hardware vendors to ensure cluster performance meets expectations.

Own product direction for Soperator, a Slurm-on-Kubernetes control plane for GPU clusters. Drive discovery, design, delivery and adoption; lead customer research; prioritize roadmap and metrics; coordinate across compute, storage, networking, observability and IAM teams; lead open-source strategy and community adoption.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.