Role OverviewJoin the Foundational Modeling team at Splunk, where we advance the state of AI for high-volume, real-time, multi-modal machine-generated data. You'll contribute to the research, design, and development of large-scale foundation models for machine-generated data, with a primary focus on graph data and additional support for logs, time series, traces, and event modalities.
What You Will Do
Develop and enhance distributed training and inference workflows, leveraging data-driven approaches to improve model quality, scalability, and operational efficiency. Collaborate with engineering, product, and data science teams to understand requirements, incorporate stakeholder feedback, and deliver AI/ML solutions that address business and technical needs.
Why It Might Be a Fit
Take ownership of assigned projects and deliver high-quality results with urgency, while proactively identifying obstacles, driving resolution of technical issues, and continuously improving development processes.
Requirements
- Master Degree in Computer Science, or related quantitative field, plus 2+ years of industry research experience
- Proven track record in at least one of the following areas: Large-scale graph representation learning and Graph Neural Networks (GNNs), large language modeling for structured and unstructured data, multi-modal fusion of graph, text, log, and time-series data
- Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience translating research ideas into production systems
Benefits
- medical, dental and vision insurance
- 401(k) plan with a Cisco matching contribution
- paid parental leave
- short and long-term disability coverage
- basic life insurance
- paid time away
- optional 10 paid days per full calendar year to volunteer
- annual bonuses
- performance-based incentive pay on top of base salary
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