Senior Software Architect – Network Monitoring Platform San Francisco, California, United

Senior Staff Software Architect – Network Monitoring Platform

About IonQ: IonQ, Inc. (NYSE: IONQ) is the world’s leading quantum platform and merchant supplier, delivering integrated quantum solutions across computing, networking, sensing, and security. IonQ’s newest generation of quantum computers, the IonQ Tempo, is the latest in a line of cutting‑edge systems that have helped customers and partners such as Amazon Web Services and AstraZeneca achieve 20x performance results and accelerate innovation in drug discovery, materials science, financial modeling, logistics, cybersecurity, and defense.

In 2025, the company achieved 99.99% two‑qubit gate fidelity, setting a world record in quantum computing performance. Headquartered in College Park, Maryland, IonQ has operations in California, Colorado, Massachusetts, Tennessee, Washington, Italy, South Korea, Sweden, Switzerland, Canada, and the United Kingdom. IonQ’s services are available through all major cloud providers and across land, sea, air, and space.

Location: This position can work onsite or hybrid from the San Francisco Bay Area.
Travel: Up to 15%, domestic or international.
Job : 1558

The Role: We are looking for a Senior Staff Software Architect to lead the design and evolution of our next‑generation Network Monitoring Platform. You’ll be part of a cross‑functional team whose mission is to lead IonQ on its journey to build the world’s best quantum computers to solve complex problems.

In this role, you will own the end‑to‑end architecture of a high‑scale, data‑intensive network monitoring platform — from data modeling and ingestion pipelines to cloud‑native deployment. You are a hands‑on technical leader who writes production code, drives critical architectural decisions, and has deep experience building systems that monitor large fleets of devices  ideal candidate brings strong Go and/or Rust expertise and thrives at the intersection of systems engineering and distributed data platforms.

• Architect cloud‑native ingestion and processing pipelines capable of handling telemetry from hundreds of thousands of devices, leveraging streaming platforms and time‑series storage at scale.
• Define and own the platform’s unified YANG‑based data model, spanning device configuration state, operational telemetry, and topology relationships across heterogeneous network environments.
• Write and review production‑quality code in Go and/or Rust for performance‑critical platform components including collectors, aggregators, and real‑time processing engines.
• Define and enforce data partitioning, sharding, and retention strategies to sustain high‑volume device event streams while meeting latency and durability requirements.
• Lead the design of cloud‑native deployment patterns (Kubernetes, service mesh, autoscaling) to ensure the platform operates reliably at global scale across multi‑cloud environments.
• Evaluate and drive adoption of purpose‑fit technologies — time‑series databases, graph stores, stream processors — with a focus on operational scalability and long‑term maintainability.
• Partner with Product and Engineering to translate monitoring requirements into platform capabilities, ensuring architectural decisions are reflected in roadmap milestones and engineering specifications.
• Mentor senior engineers through architecture reviews, coding standards, and hands‑on technical guidance, raising the bar for platform quality and engineering practices.

• 12+ years of experience in software or platform architecture, with at least 5 years leading architecture for large‑scale, data‑intensive distributed systems.
• Substantial experience building and operating device monitoring systems at scale — with direct exposure to telemetry ingestion pipelines, fleet management, and real‑time alerting across large device populations.
• Proven hands‑on coding ability; production experience with Go and/or Rust is strongly preferred for this role.
• Deep expertise in data modeling for high‑volume systems, including schema design, time‑series storage (Influx
  
  DB/Timescale), event streaming (Kafka/Kinesis), and graph data models for network topology.
• Solid understanding of…