Applied AI Scientist

The application window is expected to close on: 07/31/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received . 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, multi‑cloud environments. Join the Foundational Modeling team at Splunk, where we advance the state of AI for high‑volume, real‑time, multi‑modal 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 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. 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. Share emerging ideas, technical insights, and best practices with teammates, contributing to technical discussions and helping advance team capabilities and project outcomes. Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations to improve workflows, solve technical challenges, and support the team’s roadmap and objectives. 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. Minimum Qualifications: Master Degree in Computer Science, or related quantitative field, plus 2+ years of industry research experience. 1+ year of experience in at least one of the following areas: Large-scale graph representation learning and Graph Neural Networks (GNNs) (e.g., GCN/GAT/GraphSAGE, heterogeneous GNNs, graph transformers), large language modeling for structured and unstructured data, multi-modal fusion of graph, text, log, and time-series data. 2+ years of experience in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) 1+ year experience translating research ideas into production systems. Preferred Qualifications: Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE), spatio-temporal GNNs, heterogeneous graphs (HGNN/Relational GNNs), and knowledge-graph-augmented modeling. Expertise in constructing and operating on large-scale graphs (entity graphs, service dependency graphs, topology graphs, causal graphs, or log-event graphs). Hands-on experience with frameworks such as PyTorch Geometric (PyG), DGL, GraphGym, GraphML systems, or custom GNN runtimes. Advanced Anomaly Detection with Graph: Track record developing hybrid graph-temporal approaches (e.g., GNN + Transformer, graph contrastive learning, dynamic graph forecasting) for detecting anomalies in high-volume operational data. Hands-on experience developing, fine-tuning, or adapting foundation models for domain-specific data such as logs, time-series, graphs, operational telemetry, or enterprise knowledge, including representation learning across structured and unstructured modalities. 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. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint. Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. We are Cisco, and our power starts with you. Message to applicants applying to work in the U.S. and/or Canada: The starting salary range posted for this position is $168,000.00 to $212,400.00 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. Individual pay is determined by the candidate's hiring location, market conditions, job-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process. U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time. U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies: 10 paid holidays per full calendar year, plus 1 floating holiday for non-exempt employees 1 paid day off for employee’s birthday, paid year-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco Non-exempt employees** receive 16 days of paid vacation time per full calendar year, accrued at rate of 4.92 hours per pay period for full-time employees Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations) 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next Additional paid time away may be requested to deal with critical or emergency issues for family members Optional 10 paid days per full calendar year to volunteer For non-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies. Employees on sales plans earn performance-based incentive pay on top of their base salary, which is split between quota and non-quota components, subject to the applicable Cisco plan. For quota-based incentive pay, Cisco typically pays as follows: .75% of incentive target for each 1% of revenue a