Lead AI Researcher for Autonomous Network World Models

020 Cisco Systems, Inc.; Milpitas (CA); On-site; USD 296,000 - 375,000; Full time

Job summary

Cisco Systems, Inc. in Milpitas seeks a Principal AI Researcher to lead research on world models and learning-based approaches for autonomous networks.

You will develop models that learn network state and dynamics, and explore planning and representation learning to improve operability and safety at scale. You will publish at AI/ML conferences, collaborate with networking experts, and contribute to open-source projects while advancing model-based RL and simulation environments for evaluation

Qualifications

• Masters in CS/ML/AI/robotics with 8+ years of research experience.
• Experience in world models, RL, model-based RL, representation learning, planning, or sequential decision-making.
• 3+ years hands-on experience building, training, and optimizing deep learning models with PyTorch or JAX, including at production/research scale.

Responsibilities

• Lead research on world models and learning-based approaches for autonomous networks.
• Develop models that learn the state and dynamics of large-scale network environments.
• Build model-based RL approaches for network optimization, troubleshooting, and remediation.
• Publish research at leading AI/ML conferences and contribute to open-source projects.

Skills

World models

Reinforcement learning

Model-based RL

Representation learning

Planning

Sequential decisions

PyTorch

JAX

Publications

Prototype development

Education

Masters in CS/ML/AI/Robotics/Autonomous Systems

Tools

PyTorch

JAX

Job description

Cisco Systems, Inc. in Milpitas seeks a Principal AI Researcher to lead research on world models and learning-based approaches for autonomous networks.

You will develop models that learn network state and dynamics, and explore planning and representation learning to improve operability and safety at scale. You will publish at AI/ML conferences, collaborate with networking experts, and contribute to open-source projects while advancing model-based RL and simulation environments for evaluation