Cisco
Senior Staff Applied AI Scientist
Join the Foundational Modeling team at Splunk, a Cisco Company, to advance the state of AI for high‑volume, real‑time, multi‑modal machine‑generated data, including logs, time series, traces, and events. You’ll help design and deploy foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform.
Your Impact
Own the full lifecycle of research, design, and deployment for large‑scale foundation models targeting machine‑generated data — with a primary focus on logs, complemented by time series, traces, and event modalities.
Drive optimization of distributed training and inference pipelines to balance accuracy, performance, and cost at scale.
Partner closely with engineering, product, and data science teams to align AI solutions with both technical requirements and strategic business objectives.
Elevate organizational expertise by mentoring talent, driving strategic technical forums, and guiding research from inception to product deployment.
Shape the AI/ML vision by anticipating industry‑defining advancements and strategically embedding them into the team’s technology roadmap.
Minimum Qualifications
PhD in Computer Science, or related quantitative field, plus 5+ 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 learning‑based 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.
Preferred Qualifications
Background in building and adapting large‑scale language models (e.g., T5, BERT, LLaMA) for specialized domains including structured/unstructured logs, text, and event sequences.
In‑depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification.
Experience creating robust, scalable approaches for high‑volume, real‑time logs data.
Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
Publications in top AI/ML conferences or journals demonstrating contributions to state‑of‑the‑art methods and real‑world applications.
Solve real‑world problems, build cutting‑edge AI systems, and help build a safer, more resilient digital world. Join us for a culture that values technical excellence, impact‑driven innovation, and cross‑functional collaboration.
#J-18808-Ljbffr
Join the Foundational Modeling team at Splunk, a Cisco Company, to advance the state of AI for high‑volume, real‑time, multi‑modal machine‑generated data, including logs, time series, traces, and events. You’ll help design and deploy foundation models that enhance reliability, strengthen security, prevent downtime, and deliver predictive insights across Splunk Observability, Security, and Platform.
Your Impact
Own the full lifecycle of research, design, and deployment for large‑scale foundation models targeting machine‑generated data — with a primary focus on logs, complemented by time series, traces, and event modalities.
Drive optimization of distributed training and inference pipelines to balance accuracy, performance, and cost at scale.
Partner closely with engineering, product, and data science teams to align AI solutions with both technical requirements and strategic business objectives.
Elevate organizational expertise by mentoring talent, driving strategic technical forums, and guiding research from inception to product deployment.
Shape the AI/ML vision by anticipating industry‑defining advancements and strategically embedding them into the team’s technology roadmap.
Minimum Qualifications
PhD in Computer Science, or related quantitative field, plus 5+ 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 learning‑based 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.
Preferred Qualifications
Background in building and adapting large‑scale language models (e.g., T5, BERT, LLaMA) for specialized domains including structured/unstructured logs, text, and event sequences.
In‑depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification.
Experience creating robust, scalable approaches for high‑volume, real‑time logs data.
Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights.
Experience optimizing model architectures, distributed training pipelines, and inference efficiency to minimize cost and latency while preserving accuracy.
Fluency in automated retraining, drift detection, incremental updates, and production monitoring of ML models.
Publications in top AI/ML conferences or journals demonstrating contributions to state‑of‑the‑art methods and real‑world applications.
Solve real‑world problems, build cutting‑edge AI systems, and help build a safer, more resilient digital world. Join us for a culture that values technical excellence, impact‑driven innovation, and cross‑functional collaboration.
#J-18808-Ljbffr