Applied AI Scientist

Overview In this role you will contribute to research, design and development of large-scale foundation models for machine-generated data. You will focus on graph data while supporting logs, time series, traces, and events, collaborating with cross-functional teams to meet business needs. Your work will aim to improve model quality, scalability and operational efficiency in real-time enterprise environments. This is an opportunity to shape AI foundation models that enhance reliability, security and predictive insights at scale. You will join a culture that values technical excellence and impact-driven collaboration, with the chance to influence Splunk Observability, Security and Platform initiatives. Compensation / Benefitsmedical, dental and vision insurance 401(k) with company matchpaid parental leavepaid time off and flexible vacationrestricted stock units (RSUs)volunteer days Responsibilities Research and develop large-scale foundation models for machine-generated data, emphasizing graph data Design and enhance distributed training and inference workflows to improve scalability and efficiency Collaborate with engineering, product and data science teams to translate requirements into AI/ML solutions Share technical insights and best practices to advance team capabilities and project outcomes Explore and evaluate new AI/ML techniques and tools to support roadmaps and objectives Own projects end-to-end, identify obstacles and improve development processes with urgency Key requirements Master in Computer Science or related quantitative field + 2+ years of industry research Proven track record in graph representation learning or large-language modeling or multi-modal fusion Strong Python skills and proficiency with deep learning frameworks (PyTorch, Tensor Flow)Experience translating research ideas into production systems Collaborative communication Proactive problem solving Ownership and accountability Graph neural networks (GCN, GAT, GraphSAGE) and related graph transformers Large language modeling for structured and unstructured data Multi-modal fusion of graph, text, log, and time-series data