Cisco Systems, Inc.
Meet the Team
Splunk, a Cisco company, is building a safer, more resilient digital world with an end‑to‑end, full‑stack platform designed for hybrid, multicloud environments. Join the Foundational Modeling team at Splunk, where we advance the state of AI for high‑volume, real‑time, multimodal machine‑generated 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, impact‑driven innovation, and cross‑functional collaboration – all within a flexible, growth‑oriented environment.
Your Impact
Lead end‑to‑end research, design, and deployment of large‑scale foundation models for machine‑generated data – primarily time‑series, augmented with logs, traces, and events.
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.
Mentor and inspire team members, facilitate high‑impact technical discussions, and guide research from concept through production deployment.
Stay at the forefront of AI/ML innovations, leveraging emerging advancements to strategically influence and evolve the organization’s technology roadmap.
Minimum Qualifications:
PhD in Computer Science, or related quantitative field, plus 3+ years of industry research experience.
Proven track record in at least one of the following areas: large language modeling for both structured and unstructured data, deep‑learning‑based time‑series modeling, advanced anomaly detection, and multimodality modeling.
Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
Experience translating research ideas into production systems.
Preferred Qualifications:
Deep Learning for Time Series & Forecasting – Proven expertise designing and deploying architectures such as temporal transformers, temporal convolutional networks, and spatial–temporal models.
Advanced Anomaly Detection – Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high‑volume, real‑time time‑series data.
Multi‑Modal 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.
Large‑Scale 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 state‑of‑the‑art methods and real‑world applications.
Compensation The starting salary range posted for this position is $212,300.00 to $275,800.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.
#J-18808-Ljbffr
Your Impact
Lead end‑to‑end research, design, and deployment of large‑scale foundation models for machine‑generated data – primarily time‑series, augmented with logs, traces, and events.
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.
Mentor and inspire team members, facilitate high‑impact technical discussions, and guide research from concept through production deployment.
Stay at the forefront of AI/ML innovations, leveraging emerging advancements to strategically influence and evolve the organization’s technology roadmap.
Minimum Qualifications:
PhD in Computer Science, or related quantitative field, plus 3+ years of industry research experience.
Proven track record in at least one of the following areas: large language modeling for both structured and unstructured data, deep‑learning‑based time‑series modeling, advanced anomaly detection, and multimodality modeling.
Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
Experience translating research ideas into production systems.
Preferred Qualifications:
Deep Learning for Time Series & Forecasting – Proven expertise designing and deploying architectures such as temporal transformers, temporal convolutional networks, and spatial–temporal models.
Advanced Anomaly Detection – Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high‑volume, real‑time time‑series data.
Multi‑Modal 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.
Large‑Scale 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 state‑of‑the‑art methods and real‑world applications.
Compensation The starting salary range posted for this position is $212,300.00 to $275,800.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation*, equity, or benefits.
#J-18808-Ljbffr