Senior Distributed Systems Engineer, IonQ

• 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
• 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

Desirable

• 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. 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
• 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
• Instrument all services with structured logging, distributed tracing, and metrics (OpenTelemetry, Prometheus) so that production issues can be diagnosed and resolved within defined SLOs
• Cloud-Native Infrastructure
• 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
• Collaboration & Engineering Quality
• 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