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# Applied AI Scientist

- **Company:** Cisco Systems, Inc.
- **Location:** Portland, OR, United States
- **Salary:** $168,000.0 - $212,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Computing Environment, High-Level Architecture, Python (Programming Language), Machine Learning, Language Modeling, Operational Data Store, Tensorflow, Unstructured Data, Graph Neural Networks, Pytorch, Deep Learning, Information Technology, Data Analytics, Machine Learning Operations, Feature Extraction
- **Published:** September 30, 2026
- **Apply:** https://www.portlandjobsite.com/job.asp?id=3412417506&tx=UT6056THI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* 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., * 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.

## Description

* 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.


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