below marketmost similar roles pay hereabove market
This role pays more than
75%
of similar roles. Most pay
$180,750–$248,912
— the blue band above.
At the midpoint, this role pays about
$249k
versus about
$215k
for comparable roles.
Based on 240 similar postings.
Employer
About Cisco
Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity
Cisco currently has
307 open roles
on FindRole.
Listed pay typically runs
$167,700–$245,000
across 224 roles with salary data.
The Lead AI Researcher joins the Cisco AI Research team to architect proprietary intelligence for networking, security, and observability domains. This role involves leading high-stakes, end-to-end pre-training initiatives to build purpose-built large language models tailored for infrastructure automation and human-computer interaction. The researcher will drive advanced pre-training projects, including dataset curation, tokenization strategy, and training pipeline execution. Key responsibilities include conducting innovative generative AI research, collaborating with cross-functional teams to integrate breakthroughs into business solutions, and publishing in top-tier venues like NeurIPS or ICML. Required technical skills include experience with PyTorch or TensorFlow on distributed computing clusters of 100+ GPUs. The work focuses on solving complex system-level problems by applying machine learning to networking, data center, security, and observability datasets.
Lead the development of purpose-built large language models for networking, security, and observability infrastructure.
Drive advanced pre-training initiatives to build foundational models that provide competitive value across the product portfolio.
Conduct innovative research in generative AI to improve model capabilities for networking automation and human-computer interaction.
Execute full-cycle pre-training projects including dataset curation, tokenization strategy, and training pipeline execution.
Implement and train models on distributed computing clusters using PyTorch or TensorFlow.
Translate abstract research findings into innovative, scalable product features for business solutions.
Publish research in top-tier AI venues to establish the company as a global thought leader.
What we're looking for
Bachelor’s degree in STEM with 12+ years of experience, a Master’s degree in STEM with 8+ years of experience, or a PhD in STEM with 5+ years of research experience.
Experience leading or contributing to at least one full-cycle pre-training, distillation based pre-training, or continual pre-training project for an LLM or foundational model.
Minimum of 3 peer-reviewed publications in top-tier AI venues such as ACL, EMNLP, ICLR, ICML, NAACL, or NeurIPS.
3+ years of experience utilizing PyTorch or TensorFlow to implement and train models on distributed computing clusters with 100+ GPUs.
2+ years of experience applying machine learning models to networking, data center, security, or observability datasets to solve complex system-level problems.
Experience working in an industrial research lab environment, driving projects from conceptual research to practical application (preferred).
Subject matter expertise in networking, cybersecurity, or observability, and the ability to apply AI to these complex domains (preferred).
Strong interpersonal and communication skills with the ability to influence technical direction and build consensus among stakeholders (preferred).
Generative AI
Large Language Models (LLMs)
PyTorch
TensorFlow
Hugging Face
Machine Learning Models (MLMs)
Multi-modal models
Representation learning
Pre-training
post training
Fine-tuning
Foundation models
Generative AI
LLMs
PyTorch
TensorFlow
Hugging Face
Machine Learning Models
Representation Learning
Multi-modal Models
Pre-training
post training
Fine-tuning
Foundational Models
Generative AI
Large Language Models
Deep Learning
Representation Learning
Structured Prediction
Natural Language Processing
Transfer Learning
Data Governance
Cybersecurity
Networking
Cloud Infrastructure
AI Research
Machine Learning
Data Privacy
Artificial Intelligence
Generative AI
Large Language Models
Deep Learning
Natural Language Processing
Time Series Modeling
Unsupervised Learning
Distributed Training
GPU
Multi-GPU
Data Processing
Cybersecurity
Observability