In this high-impact role, you will design and build distributed systems and algorithms that measure, analyze, and explain network behavior across OCI’s data centers and WAN. You will combine active measurements, network topology, routing state, flow signals, and switch telemetry to identify where latency, congestion, packet loss, and failures originate.
The broader vision is to make observability an active part of the network control loop. The platform will transform trusted, confidence-scored insights into signals that automated controllers can use to steer traffic, mitigate congestion, isolate faults, and restore network health safely. These capabilities will provide the intelligence foundation for increasingly autonomous, self-healing cloud networks while maintaining explainability, policy controls, and operational safeguards.
This position requires deep systems expertise and hands-on experience with networking, algorithms, telemetry, and distributed computing. You will develop topology-aware observability capabilities, congestion-triangulation and queue-wait reconstruction algorithms, confidence-scored fault localization, and production services that operate reliably at cloud scale.
Internal Responsibilities
Responsibilities:
- Design, implement, and maintain critical components of OCI’s Next Gen Network Observability platform.
- Build scalable, topology-aware measurement, analytics, and inference systems for data center and WAN networks.
- Develop algorithms for path reconstruction, congestion triangulation, queue-wait estimation, fault localization, root cause analysis, and customer-impact attribution.
- Ingest and correlate signals from active probes, routing and topology systems, switches, ASICs, queues, flows, logs, metrics, events, and streaming telemetry.
- Produce ranked, confidence-scored diagnoses that localize problems to the responsible path, link, queue, device, or shared network resource.
- Convert observability insights into safe, explainable, and controller-consumable signals for automated traffic steering, congestion mitigation, fault isolation, and service recovery.
- Design control-loop safeguards that account for uncertainty, stale or conflicting evidence, policy constraints, and unintended interactions between automated actions.
- Design systems that remain accurate and available despite missing or delayed telemetry, topology changes, clock uncertainty, and partial network failures.
- Build validation frameworks that measure detection and localization time, accuracy, false-positive rates, confidence calibration, corrective-action effectiveness, and resilience across complex failure scenarios.
- Apply AI-assisted software development lifecycle practices across design, implementation, testing, debugging, code review, documentation, and operational analysis while maintaining engineering, security, and quality standards.
- Write robust, well-tested production code in languages such as Java, Go, C++, Rust, or Python.
- Own and resolve complex production issues involving distributed services, large-scale telemetry pipelines, real-time network state, and automated control workflows.
- Lead design, and code reviews while maintaining high standards for correctness, scalability, reliability, safety, and operational readiness.
- Mentor engineers and help establish strong engineering practices for network measurement, analytics, inference, and automated remediation systems.
- Partner with network controller, SRE, hardware, network operations, security, AI/ML, and product teams to deliver closed-loop capabilities end to end.
Preferred Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent professional experience.
- 6-10+ years of experience building large-scale distributed systems, network modeling software, network measurement platforms, or telemetry and observability systems.
- Strong knowledge of networking fundamentals, including L2/L3 forwarding, routing, switching, BGP, ECMP, data center fabrics, queues and buffers, congestion, and packet loss.
- Advanced programming experience in at least one language such as Java, Go, C++, Rust, or Python.
- Strong understanding of algorithms, distributed systems, data structures, system reliability, and performance engineering.
- Experience with one or more of the following: active network measurement, streaming telemetry, time-series or stream processing, graph algorithms, statistical inference, probabilistic modeling, or multi-source data fusion.
- Familiarity with ML pipelines and models, including data preparation, training and evaluation, deployment, versioning, monitoring, and integration into production systems.
- Hands-on experience using AI-assisted development tools and workflows across the software development lifecycle.
- Experience building systems that process high-volume, time-sensitive data and operate reliably under incomplete, delayed, or contradictory inputs.
- Demonstrated ability to validate complex algorithms and models against ground truth and translate experimental results into production-quality systems.
- Experience designing automated or closed-loop systems with appropriate safety controls, rollback mechanisms, and human oversight.
- Track record of technical leadership, mentorship, and delivery across complex, cross-functional projects.
- Experience operating production systems in cloud-scale or large enterprise environments.
Join the OCI Networking – Next Gen Network Observability team to build the intelligence and automation foundation for self-healing networks—where measurement, inference, and automated control work together to detect problems, select safe corrective actions, and restore network health at cloud scale —helping Oracle’s cloud networks operate with greater visibility, resilience, and efficiency.
External Responsibilities
Responsibilities:
- Design, implement, and maintain critical components of OCI’s Next Gen Network Observability platform.
- Build scalable, topology-aware measurement, analytics, and inference systems for data center and WAN networks.
- Develop algorithms for path reconstruction, congestion triangulation, queue-wait estimation, fault localization, root cause analysis, and customer-impact attribution.
- Ingest and correlate signals from active probes, routing and topology systems, switches, ASICs, queues, flows, logs, metrics, events, and streaming telemetry.
- Produce ranked, confidence-scored diagnoses that localize problems to the responsible path, link, queue, device, or shared network resource.
- Convert observability insights into safe, explainable, and controller-consumable signals for automated traffic steering, congestion mitigation, fault isolation, and service recovery.
- Design control-loop safeguards that account for uncertainty, stale or conflicting evidence, policy constraints, and unintended interactions between automated actions.
- Design systems that remain accurate and available despite missing or delayed telemetry, topology changes, clock uncertainty, and partial network failures.
- Build validation frameworks that measure detection and localization time, accuracy, false-positive rates, confidence calibration, corrective-action effectiveness, and resilience across complex failure scenarios.
- Apply AI-assisted software development lifecycle practices across design, implementation, testing, debugging, code review, documentation, and operational analysis while maintaining engineering, security, and quality standards.
- Write robust, well-tested production code in languages such as Java, Go, C++, Rust, or Python.
- Own and resolve complex production issues involving distributed services, large-scale telemetry pipelines, real-time network state, and automated control workflows.
- Lead design, and code reviews while maintaining high standards for correctness, scalability, reliability, safety, and operational readiness.
- Mentor engineers and help establish strong engineering practices for network measurement, analytics, inference, and automated remediation systems.
- Partner with network controller, SRE, hardware, network operations, security, AI/ML, and product teams to deliver closed-loop capabilities end to end.
Preferred Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent professional experience.
- 6-10+ years of experience building large-scale distributed systems, network modeling software, network measurement platforms, or telemetry and observability systems.
- Strong knowledge of networking fundamentals, including L2/L3 forwarding, routing, switching, BGP, ECMP, data center fabrics, queues and buffers, congestion, and packet loss.
- Advanced programming experience in at least one language such as Java, Go, C++, Rust, or Python.
- Strong understanding of algorithms, distributed systems, data structures, system reliability, and performance engineering.
- Experience with one or more of the following: active network measurement, streaming telemetry, time-series or stream processing, graph algorithms, statistical inference, probabilistic modeling, or multi-source data fusion.
- Familiarity with ML pipelines and models, including data preparation, training and evaluation, deployment, versioning, monitoring, and integration into production systems.
- Hands-on experience using AI-assisted development tools and workflows across the software development lifecycle.
- Experience building systems that process high-volume, time-sensitive data and operate reliably under incomplete, delayed, or contradictory inputs.
- Demonstrated ability to validate complex algorithms and models against ground truth and translate experimental results into production-quality systems.
- Experience designing automated or closed-loop systems with appropriate safety controls, rollback mechanisms, and human oversight.
- Track record of technical leadership, mentorship, and delivery across complex, cross-functional projects.
- Experience operating production systems in cloud-scale or large enterprise environments.
Join the OCI Networking – Next Gen Network Observability team to build the intelligence and automation foundation for self-healing networks—where measurement, inference, and automated control work together to detect problems, select safe corrective actions, and restore network health at cloud scale —helping Oracle’s cloud networks operate with greater visibility, resilience, and efficiency.
