Top Tech Jobs & Startup Jobs in Belfast

Own HRIS automation and integrations: audit People Ops workflows, design and implement automated processes, manage HiBob and Recruitee configurations, build API-driven integrations, enforce data governance, create dashboards and documentation to improve global People Operations efficiency.

The Senior Applied ML Engineer will architect backend systems for a media intelligence platform, integrating AI/ML services, optimizing workflows, and overseeing large media processing pipelines.

Own portfolio data and reporting infrastructure: ensure accurate CRM entry, collect and reconcile portfolio reports, produce dashboards, manage inbound dealflow and pipeline hygiene, support deal execution and closing, liaise with internal/external parties, and continuously improve processes and automation.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will innovate in model compression techniques for large multimodal AI systems, focusing on efficiency and model deployment.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.

As an AI Research Engineer, you will optimize model serving and inference architectures, focusing on high-performance, resource-efficient solutions for AI systems across various deployment environments.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Drive research and engineering for large-scale LLM and multi-modal pre-training: design architectures, curate massive datasets, run distributed training on multi-node GPU clusters, optimize performance, and advance cross-modal alignment and efficiency.