Posted 4 weeks ago

Senior Distributed Systems Engineer

IonQ

Santa Clara, CA, United States 🇺🇸

Onsite
Full Time
AWS
Azure
Google Cloud
Kafka

$163,430 - $213,972 year

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We are looking for a deeply hands-on Senior 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 work closely with the Principal Architect to translate platform architecture decisions into reliable, well-tested, production-grade implementations
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
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
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
Instrument all services with structured logging, distributed tracing, and metrics (OpenTelemetry, Prometheus) so that production issues can be diagnosed and resolved within defined SLOs
Package and deploy services as containerized workloads on Kubernetes, maintaining infrastructure-as-code (Terraform) across AWS, GCP, and Azure environments
Contribute to multi-cloud deployment patterns that meet platform availability targets and data residency requirements
Partner with the Principal Architect to translate platform architecture decisions into tested, production-grade implementations with clear engineering specifications
Lead code reviews and mentor junior engineers through pairing and technical guidance, raising the team’s overall bar for correctness and maintainability
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
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
Strong engineering practices: test-driven or test-informed development, code review, CI/CD, and operational ownership including on-call
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 — you write these languages regularly and can demonstrate production systems you have built with them
Familiarity with observability tooling — structured logging, metrics (Prometheus/Grafana or equivalent), and distributed tracing (OpenTelemetry or equivalent)
Experience with cloud-native deployment: containerization (Docker), Kubernetes orchestration, and operating services across at least one major cloud provider (AWS, GCP, or Azure)
Comfortable writing SQL and working with both relational and time-series data models; experience with schema design and query optimization
Experience with network telemetry protocols (gNMI, NETCONF, SNMP) or prior work at a network equipment vendor or network management platform company (e.g., Juniper, Cisco, Nokia)
Hands-on experience with graph databases or graph data models for network topology (e.g., Neo4j, Amazon Neptune, or custom adjacency representations)
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)

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