Senior Distributed Systems Engineer
Posted Updated
- 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)
Skills
- API
- AWS
- Azure
- CI/CD
- Cisco
- Cloud
- Cloud Native
- Containerization
- Data Modeling
- Distributed Systems
- Docker
- Fedramp
- GCP
- Grafana
- gRPC
- InfluxDB
- Infrastructure as Code
- Kafka
- Kinesis
- Kubernetes
- Neo4j
- Networking
- Nist
- Observability
- OpenTelemetry
- Prometheus
- REST
- Rust
- SaaS
- SNMP
- SQL
- Technical Writing
- Terraform
- Time Series
- TimescaleDB