Lead Principal Software Engineer, Core Infrastructure

Lead Principal Software Engineer — AI Infrastructure, Network Modeling & Control

Build the network control software that enables OCI’s AI infrastructure to scale. Large-scale AI training and inference depend on reliable, high-performance networks connecting GPU infrastructure. Oracle Cloud Infrastructure’s Network Modeling and Control team builds the software that models these physical networks and enables their configuration, expansion, and safe operation.

Our services help turn infrastructure designs into working networks and coordinate changes as capacity grows. You will tackle distributed systems challenges at the intersection of software, networking, and AI infrastructure: maintaining consistent network state, coordinating concurrent changes, recovering from partial failures, and introducing automation that protects availability.

We are looking for a hands-on Lead Principal Software Engineer to shape the architecture and evolution of these services. Your decisions will help determine how efficiently OCI brings new AI network capacity online and how reliably that infrastructure operates.

This is an individual contributor role with technical influence across teams. You will define architecture, write and review critical code, lead complex initiatives through production, and mentor engineers.

What makes this work interesting:

• Direct impact on AI infrastructure. Build foundational software for the networks supporting GPU clusters and AI workloads.
• Software that controls physical systems. Translate network topology and intended configuration into safe, coordinated infrastructure changes.
• Challenging distributed systems problems. Address concurrency, consistency, scheduling, and failure recovery in services where correctness affects network availability.
• Architectural influence as infrastructure grows. Shape how our services accommodate larger networks, new topologies, and evolving AI infrastructure requirements.

What you will do:

• Lead architecture and hands-on development for distributed services supporting network modeling, configuration, and lifecycle operations across OCI’s AI and cloud infrastructure.
• Evolve network models, APIs, and automation to support expanding GPU infrastructure and new network architectures.
• Design reliable workflows for concurrent and long-running changes, with clear approaches to state management, retries, recovery, and rollback.
• Build safety into network automation through validation, controlled rollout, observability, and limits on the scope of failures.
• Improve service performance and scalability to accelerate infrastructure provisioning and network changes.
• Partner with network engineering, infrastructure delivery, operations, and adjacent software teams to bring new capabilities into production.
• Own major technical initiatives from problem definition through implementation, rollout, and measurable production outcomes.
• Mentor engineers and use production experience to improve architecture, reduce recurring failures, and eliminate manual work.

What you bring:

• 10+ years of software engineering experience, including substantial experience designing and operating distributed systems.
• Strong programming skills and a record of delivering complex production software.
• Deep understanding of concurrency, consistency, fault tolerance, API design, and persistent state.
• Experience making architectural decisions across services and explaining their tradeoffs clearly.
• Evidence of leading complex initiatives across teams while remaining close to implementation.
• Strong judgment about reliability, operational readiness, and safe infrastructure changes.

Experience that would help you succeed:

• Infrastructure software supporting AI/ML platforms, GPU clusters, or large-scale compute environments.
• Network automation, network modeling, configuration management, or infrastructure control systems.
• Workflow orchestration, reconciliation, or scheduling across distributed resources.
• Model-based testing, formal methods, or other techniques for verifying complex state transitions.

You do not need prior experience with every technology in our environment. We value strong systems fundamentals, sound engineering judgment, and the ability to learn a complex domain.