Software Engineering Technical Leader

Lead Software Engineer

This is a hybrid role based out of Cisco's Seattle or San Jose office.

The Cisco AI Research team is a dynamic group of scientists and engineers dedicated to building the future of proprietary intelligence. Our core mission is to develop purpose-built large language models tailored specifically for Cisco's networking, security, and observability domains. We operate at the intersection of basic and applied research, ensuring that Cisco maintains its technological edge without reliance on external AI providers.

Our team is composed of doers and experts who thrive in a collaborative environment, blending deep technical rigor with a shared passion for solving complex, real-world problems at a global scale.

As a Lead Software Engineer, you will build secure, cloud-native networking and simulation platforms that power Cisco's domain-specific LLMs. Partnering across AI research, security, product, and infrastructure engineering, you will architect simulation topologies, automate controller ecosystems, and translate cutting-edge AI and networking concepts into robust, production-grade capabilities.

• Architect high-fidelity, secure network topologies using Cisco Modeling Labs (CML) and establish data contracts and validation pipelines to ensure provenance and integrity for AI training and evaluation data.
• Lead the secure automation and integration of Cisco controllers (Nexus Dashboard, Catalyst Center, Intersight, Cisco Hyper Fabric) across on-premises, hybrid, and cloud-native environments.
• Design, build, and test high-performance distributed software, setting engineering standards for network programmability, cloud architecture, and simulation-driven workflows.
• Apply zero-trust principles, threat modeling, RBAC/IAM, and secrets management to secure controller integrations, simulation platforms, and data pipelines.
• Implement telemetry and monitoring for simulation performance and cloud infrastructure, driving postmortems and root-cause analyses into preventive platform controls.
• Mentor peers through design reviews and knowledge sharing, shape the engineering roadmap, and communicate technical trade-offs to cross-functional stakeholders.

Minimum Qualifications:

• Bachelor's degree in computer science or related field + 8 years of related experience, OR Master's degree in STEM + 6 years of related experience, OR PhD in STEM + 3 years of related experience (or equivalent related work experience).
• 5+ years of professional software engineering experience designing, developing, and testing production-grade software using modern programming languages (e.g., Python, Go, C++, or Java).
• 4+ years of professional experience in enterprise/datacenter networking protocols (e.g., TCP/IP, BGP, EVPN, VXLAN) and network simulation/modeling platforms (e.g., Cisco Modeling Labs [CML], EVE-NG, or GNS3).
• 3+ years of experience with cloud-native infrastructure, distributed systems architecture, and containerization technologies (e.g., Docker, Kubernetes).
• 3+ years of experience integrating and automating network controllers, SDN platforms, or infrastructure management systems via REST APIs/SDKs (e.g., Nexus Dashboard, Catalyst Center, Intersight).
• 2+ years of experience implementing secure software development lifecycle (SSDLC) practices, including threat modeling, Role-Based Access Control (RBAC), and secrets management.

Preferred Qualifications:

• Proven experience developing, securing, and validating high-fidelity simulation models within Cisco Modeling Labs (CML).
• Demonstrated expertise managing and automating Cisco controller environments, including Nexus Dashboard, Catalyst Center, Intersight, Secure Firewall Management Center, and Cisco Hyper Fabric.
• Strong understanding of data-contract design, schema evolution, data provenance, and automated validation methodologies for large-scale modeling, AI training, and evaluation environments.
• Experience designing and operating cloud-native systems using containers, Kubernetes, infrastructure as code, CI/CD pipelines, APIs, distributed data services, and modern observability platforms.
• Practical knowledge of application and infrastructure security, including secure software…