> Markdown version of [/jobs/ext/1747416-applied-ai-scientist](https://www.wearedevelopers.com/jobs/ext/1747416-applied-ai-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Scientist - **Company:** Cisco Systems, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $165,300.0 - $270,300.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Distributed Computing Environment, Python (Programming Language), Machine Learning, Language Modeling, Operational Data Store, Tensorflow, Unstructured Data, Pytorch, Deep Learning, Information Technology, Data Analytics - **Published:** July 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4a75df668f89ba5f ## About the Role * Master Degree in Computer Science, or related quantitative field, plus 2+ years of industry research experience. * Proven track record 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. * Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * 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. ## Related Videos - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Graph Neural Networks: What’s behind the Hype?](https://www.wearedevelopers.com/videos/474-graph-neural-networks-what-s-behind-the-hype) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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