Principal AI Researcher

Cisco

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Seattle, WASan Jose, CA
Salary: $263,700–$333,400 / yr
Posted: 10 days ago
Freshness: Confirmed live yesterday
Closes: Oct 5, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $219k

This role $299k

$156k most similar roles pay here $352k

This role pays more than 85% of similar roles. Most pay $182,750–$256,237 — the shaded band above. At the midpoint, this role pays about $299k versus about $219k 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 196 open roles on FindRole.

Listed pay typically runs $167,700–$245,200 across 196 roles with salary data.

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At a glance

TL;DR · Principal AI Researcher

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Principal AI Researcher, AISWP (Hybrid) joins the Cisco AI Research team to develop purpose-built large language models for networking, security, and observability domains. This role involves defining the multi-year research strategy and technical architecture for proprietary intelligence systems. The researcher will lead massive-scale pre-training, continued pre-training, and reinforcement learning initiatives while architecting advanced agentic frameworks and autonomous operations to improve reasoning capabilities in IT environments. Key responsibilities include translating complex generative AI breakthroughs into scalable production ecosystems and publishing high-impact research at top-tier venues. Required expertise includes PyTorch, distributed training on large GPU clusters, and experience with domain-specific systems like cybersecurity or data center infrastructure. The role focuses on solving the challenge of moving from generalized models to specialized, expert architectures that secure and automate global network infrastructure through advanced optimization and low-latency inference pipelines.

Skills

Large Language Models PyTorch Reinforcement Learning Distributed Training Generative AI Agentic Frameworks Pre-training Inference Pipelines Context Engineering Networking Cybersecurity Data Center Infrastructure

What you'll do

Define the multi-year research strategy and technical roadmap for purpose-built large language models in networking and security.
Lead massive-scale pre-training, continued pre-training, and reinforcement learning initiatives to develop foundational models.
Architect advanced agentic frameworks and autonomous systems to improve reasoning and routing for IT environments.
Translate complex AI research breakthroughs into scalable, enterprise-grade product ecosystems for executive leadership.
Manage end-to-end training pipelines including dataset curation and distributed training on large-scale GPU clusters.
Publish high-impact research and lead workshop proposals at top-tier AI conferences to establish industry leadership.
Develop specialized models tailored specifically for Cisco’s networking, security, and observability domains.

What we're looking for

Must hold a Bachelor's in STEM with 15+ years experience, a Master's with 12+ years, or a PhD with 8+ years of research experience.
Experience architecting and leading full-cycle pre-training, continual pre-training, or reinforcement learning pipelines for large-scale foundational models.
Minimum of 6 publications in top-tier AI venues such as ACL, EMNLP, ICLR, ICML, NAACL, or NeurIPS.
At least 5 years of experience scaling training pipelines using PyTorch or similar frameworks on distributed environments with 1000+ GPUs.
At least 4 years of experience applying generative ML models to complex systems like networking, cybersecurity, or data center infrastructure.
Experience designing agentic frameworks and autonomous systems for production environments is preferred.
Expertise in advanced optimization, including context engineering, efficient serving architectures, and low-latency inference pipelines.
Strong executive communication skills to influence technical direction and mentor senior research talent.

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