Principal Machine Learning Engineer

Cisco is a leading technology company focused on creating solutions that connect and protect organizations in the AI era. They are seeking a Principal Machine Learning Engineer to define the strategic vision for AI and foundation models, lead research and deployment for large-scale models, and mentor technical talent across teams.

Responsibilities:

• Set and Drive Vision: Define and champion the strategic vision for AI and foundation models across Splunk and Cisco platforms, shaping the research and technology roadmap to anticipate and address industry‑defining challenges
• Architect and Lead Breakthroughs: Lead the end‑to‑end lifecycle of research, design, and deployment for large‑scale foundation models targeting machine‑generated data, with deep focus on logs and complementary modalities (time series, traces, events)
• Influence at Scale: Partner with executive leadership, engineering, product, and data science teams to ensure AI solutions align with broader organizational objectives, product strategies, and customer needs
• Mentorship and Thought Leadership: Cultivate organizational excellence by mentoring senior technical talent, fostering research communities, and driving best practices in AI across global teams
• Foster Innovation: Embed cutting‑edge research and technological advances into products, driving sustained competitive advantage and transformation at enterprise scale

Requirements:

• PhD in Computer Science, or related quantitative field, plus 7+ 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
• Deep NLP & Domain‑Adapted LLMs: Background in building and adapting large‑scale language models (e.g., T5, BERT, LLaMA, GPTs) for specialized domains including structured/unstructured logs, text, and event sequences
• Log Analytics Expertise – In‑depth knowledge of structured/unstructured system logs, event sequence analysis, anomaly detection, and root cause identification
• Advanced Anomaly Detection – Experience creating robust, scalable approaches (statistical, deep learning, or hybrid) for high‑volume, real‑time logs data
• Multi‑Modal AI Modeling – Strong track record fusing logs, time series, traces, tabular data, and graphs for foundation models tackling complex operational insights
• 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