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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - **Company:** Psiquantum, Corp. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $235,600.0 - $276,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Cloud Computing, Code Review, Computer Programming, Computer Engineering, Data Centers, Software Debugging, Linux, File Systems, Distributed Systems, Domain Name System (DNS), Information Lifecycle Management, Python (Programming Language), Modbus, Networking Basics, Network Connections, Online Transaction Processing, Quantum Computing, Reliability Engineering, Software Tools, Prometheus, OPC Unified Architecture, Service Discovery, Simple Network Management Protocols, TCP/IP, Data Logging, Transport Layer Security, Cloud Platform System, System Availability, Large Language Models, Grafana, Gitlab, Gitlab-ci, Kubernetes, Information Technology, Apache Kafka, Vertica, Terraform, OpenSearch, Pagerduty - **Published:** October 27, 2026 - **Apply:** https://startup.jobs/staff-software-engineer-observability-site-reliability-psiquantum-10574417 ## About the Role * 8+ years of professional software, infrastructure, or site reliability engineering experience, including significant time building and operating observability systems in production. * A Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Physics, or a closely related technical discipline is required. * Deep hands-on experience with Prometheus and Grafana, including PromQL, recording and alerting rules, Alertmanager, exporters, dashboard design, and running both reliably at scale. * Production experience with OpenTelemetry, including the Collector, OTLP, SDK and automatic instrumentation, and semantic conventions. * Hands-on experience running workloads on self-managed, on-premises Kubernetes and in the cloud, with containers, Helm, and infrastructure as code such as Terraform; practical experience with GitLab and GitLab CI is strongly preferred. * Strong programming skills in Python and at least one of Go or Rust, with the ability to write production-quality exporters, Collector components, and automation rather than only configuring off-the-shelf tools. * Solid knowledge of Linux and networking fundamentals, including processes, filesystems, resource limits, DNS, TCP/IP, and TLS, and experience debugging distributed systems from the host up. * Experience carrying a pager for production systems, leading incident response, running post-incident reviews, and designing alerts people act on. * Staff-level technical leadership: leading design work, setting standards other teams adopt, mentoring engineers, influencing across teams, and communicating tradeoffs to technical and non-technical stakeholders. * Proficiency with modern AI/LLM engineering tools, such as coding copilots and agents, model APIs, and retrieval workflows; a disciplined approach to privacy, security, hallucination risk, and generated-code review. * Ability to work closely with hardware, electronics, and research teams across sites and time zones, in a fast-moving environment where requirements evolve as the quantum computer itself evolves Nice to Haves * Experience with scalable metrics, log, and trace backends such as Thanos, Mimir, VictoriaMetrics, Loki, Tempo, OpenSearch, or ClickHouse. * Experience collecting telemetry from laboratory or industrial equipment over protocols such as Modbus, OPC UA, SNMP, SCPI, or serial interfaces, or through vendor SDKs. * Familiarity with cryogenic, vacuum, laser, or other lab systems, or with software used in laboratories, data centers, or advanced manufacturing. * Experience with high-frequency or high-cardinality time-series data, including downsampling, aggregation, and data lifecycle management. * Experience running observability across multiple sites and hybrid on-premises and cloud environments, including sites with constrained network connectivity. * Experience with eBPF-based observability, continuous profiling, or Kubernetes operators. * Experience with SLO tooling, synthetic monitoring, or incident management platforms such as PagerDuty. * Familiarity with VAST Data, Kafka, and AWS. * Contributions to OpenTelemetry, Prometheus, Grafana, or related open-source projects. * A background in physics or quantum computing is helpful but not required; curiosity, systems thinking, and motivation to learn the domain are essential. ## Description The Observability team builds and runs the telemetry platform that lets PsiQuantum see how its quantum computing systems, and the infrastructure behind them, are behaving. We collect metrics, logs, and traces from on-premises Kubernetes clusters, cloud infrastructure, and the custom hardware that makes up a photonic quantum computer, including control electronics and cryostats. Engineering, operations, hardware, and research teams rely on this data to run the machine, catch problems early, and work out what went wrong when something fails., * Design, deploy, and operate the observability platform across on-premises Kubernetes and cloud, covering collection, storage, querying, visualization, and alerting for metrics, logs, and traces. * Roll out OpenTelemetry across the fleet: deploy and configure Collectors, design telemetry pipelines, and help service teams adopt OTel SDKs and instrumentation in Python, Rust, and other languages. * Run Prometheus in production, including service discovery, scrape configuration, recording and alerting rules, Alertmanager routing, retention, high availability, and long-term storage. * Manage Grafana dashboards, alerts, and data sources as code so they are versioned, reviewed, and reproducible across environments. * Work with hardware teams to instrument custom devices, control electronics, cryostats, and other lab equipment; build the exporters and collectors that bring device telemetry into the platform, including from equipment that was never designed to be monitored. * Define and publish telemetry standards - metric and label naming, cardinality budgets, structured logging, trace context propagation, and resource attributes - so data from different teams and systems can be correlated. * Partner with service owners and operations teams to define SLOs and alerts that reflect real impact, reduce alert noise, and make every page actionable. * Take part in the team's on-call rotation: respond to incidents affecting the observability platform, help other teams diagnose production issues with telemetry, and lead blameless post-incident reviews through to completed fixes. * Run the platform as a production service in its own right, with capacity planning, cost management, upgrades, backup and recovery, and monitoring of the monitoring stack itself, delivered through GitLab CI/CD and infrastructure as code. * Use AI and LLM tooling to accelerate engineering and operational work responsibly - including coding assistants, query and dashboard generation, and incident analysis - while applying rigorous review, security, and validation practices. * Work with the team's technical lead on architecture and roadmap; lead design reviews, mentor engineers, document consequential decisions, and coordinate with software, hardware, research, infrastructure, and operations teams to turn their observability needs into a shared plan. Daily Functions * Instrument and collect: Configure OTel Collectors and Prometheus scrape targets, write exporters, and help teams instrument new services and devices. * Build and review: Write code and configuration, review merge requests for platform and dashboard changes, and simplify pipelines that are hard to understand or operate. * Work with hardware teams: Learn how a new device or cryostat behaves, agree on what to measure and alert on, and get its data flowing. * Watch and respond: Check platform health and alert quality, handle on-call pages, and help other teams debug with metrics, logs, and traces. * Shape the platform: Work through storage, retention, cost, and tooling tradeoffs with the technical lead; unblock other engineers; keep near-term work aligned with the roadmap. * Apply AI with judgment: Use LLM-assisted tools for queries, configuration, code, and documentation, then verify outputs to the standard expected for production infrastructure.