Senior Applied AI Scientist
About The Position
Splunk, a Cisco company, is building a safer, more resilient digital world with an endâtoâend, fullâstack platform designed for hybrid, multiâcloud environments. Join the Foundational Modeling team at Splunk, where we advance the state of AI for highâvolume, realâtime, multiâmodal machineâgenerated data - including logs, time series, traces, and events. We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco's global engineering capabilities. Our work spans networking, security, observability, and customer experience - designing and deploying foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform at enterprise scale. You'll be part of a culture that values technical excellence, impactâdriven innovation, and crossâfunctional collaboration - all within a flexible, growthâoriented environment.
Requirements
- PhD in Computer Science, or related quantitative field, plus 1+ years of industry research experience.
- Proven track record in at least one of the following areas: large language modeling for both structure and unstructured data, deep learningâbased time series modeling, advanced anomaly detection, and multi-modality modeling.
- Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
- Experience translating research ideas into production systems.
Nice To Haves
- Deep Learning for Time Series & Forecasting - Proven expertise designing and deploying architectures such as temporal transformers, temporal convolutional networks, and spatialâtemporal models.
- Advanced Anomaly Detection - Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for highâvolume, realâtime time series data.
- MultiâModal AI Modeling - Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
- Probabilistic Forecasting & Uncertainty Quantification - Skills in Bayesian deep learning and probabilistic models to capture and communicate predictive uncertainty.
- LargeâScale Training & Optimization - Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
- MLOps & Continuous Learning - Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
- Strong Research Track Record - Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR, ACL, KDD) demonstrating contributions to stateâofâtheâart methods and realâworld applications.
Responsibilities
- Lead the research, design, and deployment of largeâscale foundation models for machineâgenerated data - primarily time series, augmented with logs traces, and events.
- Optimize distributed training and inference pipelines to maximize accuracy, performance, and efficiency at scale.
- Work closely with engineering, product, and data science to ensure solutions meet defined technical requirements and deliver tangible business impact.
- Mentor team members and contribute directly to model architecture reviews, experimental design, and production rollout processes.
- Stay current with AI/ML developments and integrate relevant advancements into ongoing projects and technical plans.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Industry
Professional, Scientific, and Technical Services
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
