Senior Distributed Systems Engineer

IonQ•Santa Clara, CA

13h•$163,430 - $213,972•Onsite

About The Position

IonQ is seeking a hands-on Senior Distributed Systems Engineer to develop its Network and Security Platform. This role involves owning backend services such as ingestion pipelines, telemetry processing, and device management, providing real-time network visibility and quantum-safe intelligence. The systems built will be crucial for IonQ's mission to secure global networks against quantum threats. The engineer will be responsible for end-to-end ownership of backend services, including high-throughput ingestion pipelines, time-series storage, real-time processing engines, and APIs. The ideal candidate is proficient in Go and/or Rust, has strong distributed systems fundamentals, and experience building scalable, data-intensive backend systems.

Requirements

6+ years of backend software engineering experience with a strong record of shipping production services at scale.
Proven, hands-on proficiency in Go and/or Rust, with demonstrated production systems built using these languages.
Solid experience building and operating distributed data systems: event streaming (Kafka, Kinesis, or equivalent), time-series databases, and high-throughput ingestion pipelines.
Strong fundamentals in distributed systems: consistency models, failure modes, backpressure, idempotency, and exactly-once vs. at-least-once processing tradeoffs.
Experience with cloud-native deployment: containerization (Docker), Kubernetes orchestration, and operating services across at least one major cloud provider (AWS, GCP, or Azure).
Familiarity with observability tooling: structured logging, metrics (Prometheus/Grafana or equivalent), and distributed tracing (OpenTelemetry or equivalent).
Comfortable writing SQL and working with both relational and time-series data models; experience with schema design and query optimization.
Strong engineering practices: test-driven or test-informed development, code review, CI/CD, and operational ownership including on-call.

Nice To Haves

Experience with network telemetry protocols (gNMI, NETCONF, SNMP) or prior work at a network equipment vendor or network management platform company.
Hands-on experience with graph databases or graph data models for network topology.
Familiarity with YANG data modeling or prior experience working with device configuration schemas in network management contexts.
Experience building multi-tenant SaaS backend services with strong data isolation and per-tenant resource management.
Contributions to open-source infrastructure, observability, or networking projects; or published technical writing on distributed systems topics.
Experience working in or adjacent to regulated, security-conscious environments (FedRAMP, NIST, or equivalent).

Responsibilities

Design and ship production backend services in Go and/or Rust, focusing on code quality, test coverage, and timely delivery.
Build and maintain high-throughput telemetry ingestion pipelines using gNMI, NETCONF, and SNMP for reliable data collection from large device fleets.
Develop RESTful and gRPC APIs to expose platform data to internal consumers and external integrations, meeting uptime and latency SLAs.
Implement and evolve the time-series storage layer (InfluxDB, TimescaleDB, or equivalent), optimizing for performance, retention, and data fidelity.
Build event streaming pipelines using Kafka or Kinesis with at-least-once or exactly-once semantics.
Design data partitioning and archival strategies to meet ingest volume, latency, and durability SLAs.
Own service reliability end-to-end, including participation in on-call rotation, incident response, and driving systemic improvements.
Instrument services with structured logging, distributed tracing, and metrics (OpenTelemetry, Prometheus) for efficient issue diagnosis.
Package and deploy services as containerized workloads on Kubernetes, maintaining infrastructure-as-code (Terraform) across AWS, GCP, and Azure.
Contribute to multi-cloud deployment patterns that meet platform availability and data residency requirements.
Partner with the Principal Architect to translate platform architecture decisions into tested, production-grade implementations.
Lead code reviews and mentor junior engineers to improve team's overall engineering quality.