Senior Staff Distributed Systems Engineer

The Role

We are looking for a deeply hands-on Senior Staff Distributed Systems Engineer to join the team building IonQ's Network and Security Platform. You will own the backend services - ingestion pipelines, telemetry processing, device management - that give operators real-time visibility into their networks and the intelligence to make them quantum-safe. This is infrastructure that matters: the systems you build will underpin IonQ's mission to secure the world's networks against quantum threats.

In this role, you will own backend services end-to-end - from high-throughput ingestion pipelines collecting telemetry from large device fleets, to time-series storage, real-time processing engines, and the APIs that surface platform data to the rest of the organization. You are a deeply hands-on engineer who writes production-quality Go and/or Rust daily, takes pride in operational ownership, and brings strong distributed systems fundamentals to every problem you tackle.

You will partner with and at times drive platform architecture decisions alongside the Principal Architect, setting technical direction for the platform and influencing how IonQ's engineering org approaches distributed systems problems more broadly. The ideal candidate has direct experience building data-intensive backend systems at scale - and is energized by the challenge of making complex infrastructure simple, observable, and dependable.

Responsibilities

Backend Services & Production Code

• Design and ship production backend services in Go and/or Rust, measured by code quality, test coverage, and on-time delivery of service milestones.
• Build and maintain high-throughput telemetry ingestion pipelines using gNMI, NETCONF, and SNMP, ensuring reliable data collection across large device fleets.
• Develop RESTful and gRPC APIs that expose platform data to internal consumers and external integrations, evaluated by uptime, latency SLAs, and adoption by downstream teams.

Data Engineering & Storage

• Implement and evolve the time-series storage layer (InfluxDB, TimescaleDB, or equivalent), optimizing for query performance, retention policies, and data fidelity under production load.
• Build event streaming pipelines using Kafka or Kinesis with at-least-once or exactly-once semantics, validated through integration tests and production monitoring.
• Design data partitioning and archival strategies that sustain target ingest volumes while meeting defined latency and durability SLAs.

Platform Reliability & Operations

• Own service reliability end-to-end - participate in on-call rotation, lead incident response, and drive systemic improvements tracked via post-mortems and reduction in repeat incidents.