netboxlabs - Senior Full Stack Engineer, Observability

Remote - LATAM, Remote; UK, Remote; US Remote - USA *$85k - $98k3w ago

Remote Senior LATAM Artificial Intelligence Utilities Senior Full Stack Developer Full Stack Engineer React TypeScript Full Stack Go Python

Requirements

• Experience: 5+ years of professional software engineering, with meaningful production experience on both backend and frontend. • Production experience with Go and Python: strong in at least one and working proficiency in the other. • Hands-on experience designing and operating gRPC services with Protocol Buffers, including schema evolution and backward compatibility, streaming RPCs, deadlines, interceptors/middleware, and error handling. • Experience building distributed, event-driven systems, including message queues (e.g., RabbitMQ or Kafka), asynchronous job processing, and idempotent data ingestion. • Frontend (monitoring and telemetry dashboards): • Strong React and TypeScript skills, including component composition, state management, and typing best practices. • Proven experience building monitoring, observability, or analytics dashboards: time-series charts, heatmaps, status and health views, and drill-down navigation. • Hands-on experience with data visualization libraries (e.g., D3, ECharts, Recharts, uPlot, or Visx). • Experience rendering large or high-frequency datasets performantly, using techniques such as downsampling, virtualization, and canvas or WebGL rendering. • Experience handling real-time data in the browser via WebSockets, server-sent events, or gRPC-Web streaming, along with caching and refresh strategies (e.g., TanStack Query). • Modern CSS (TailwindCSS or similar utility-first frameworks), responsive layout, and practical accessibility (WCAG), including color-blind-safe palettes and accessible charts. • Automated frontend testing with Jest and React Testing Library, including visual regression testing for charts and dashboards. • AI-enabled development: • Practical, regular use of AI coding assistants and agents (e.g., Claude Code, Cursor, GitHub Copilot) across the development lifecycle: writing and refactoring code, generating tests, reviewing changes, and writing documentation. • Good judgment about when to trust AI output, including verifying generated code, keeping changes reviewable, and protecting sensitive data such as customer network information and credentials. • Familiarity with prompting and context techniques that make AI tools effective on a real codebase, such as project instructions, specs, and structured workflows. • Ways of working: • Good communication skills and a proven ability to work collaboratively in small, cross-functional teams. • Comfortable working in a fast-moving environment and contributing to product and technical decisions. • Networking protocols and device interfaces: • Familiarity with networking protocols and concepts such as TCP/IP, DNS, DHCP, BGP, OSPF, VLANs, and LLDP/CDP. • Experience with network management and telemetry interfaces such as SNMP (MIBs, polling, traps), gNMI/gNOI, streaming telemetry. • Network automation: Experience with NetBox, network automation frameworks (e.g., NAPALM, Nornir, Netmiko), or building agents that run in customer environments. • Telemetry and observability: OpenTelemetry/OTLP, Prometheus, or time-series databases (e.g., Mimir, InfluxDB, ClickHouse), including query languages such as PromQL. • Dashboard tooling: • Grafana panel or plugin development, or embedding third-party dashboards in a product. • User-configurable dashboards (saved views, custom layouts, dashboards as code). • Designing alerting and incident UX: thresholds, alert states, annotations, and event timelines. • Building with AI: • Building AI-powered product features or internal tools using LLM APIs, such as natural-language queries over telemetry, anomaly explanations, or assisted troubleshooting. • Experience with the Model Context Protocol (MCP), tool-using agents, or agentic development frameworks and workflows. • Evaluating AI features for quality and reliability, such as eval suites and guardrails.

Responsibilities

• Design, build, and operate backend services in Go and Python for discovery, assurance, fleet management, and data ingestion. • Define and evolve gRPC and REST APIs with clear, versioned contracts using Protocol Buffers and OpenAPI. • Build React and TypeScript monitoring and telemetry dashboards that show device, interface, and network health in real time, with time-series charts, status views, and drill-down from fleet to device to interface. • Design dashboard experiences that help operators spot problems fast: sensible defaults, time-range and filter controls, thresholds and alert states, and clear links from a metric to the underlying device in NetBox. • Work with backend engineers on the query and aggregation APIs that power dashboards, so they stay fast with large fleets and high-frequency telemetry. • Build type-safe integration between frontend and backend using generated API clients, shared schemas, and consistent error and auth handling. • Participate in the team's on-call rotation for the services it owns. • Add automated tests across the stack (unit, integration, contract, and end-to-end) and enforce quality gates in CI. • Collaborate with product managers, designers, customer-facing teams, and other engineering teams. The goal is for features to solve real network operator problems. • Use AI-enabled development tools and agentic workflows day to day to speed up design, coding, testing, code review, and incident triage, and help the team adopt them effectively and safely. • Review code, mentor teammates, and share best practices for service design, API design, and frontend engineering. • Participate in planning processes and help shape the roadmap.

Benefits

• Remote, LATAMRemote, LATAM $85K – $98K • Remote, UKRemote, UK £85K – £105K • Offers Equity • Offers Bonus • Remote, USRemote, US $165K – $195K • Offers Equity • Offers Bonus • Upload your resume here to autofill key application fields. • Drop your resume here! • Parsing your resume. Autofilling key fields... • or drag and drop here • Tell us about a dashboard you built that displayed large or high-frequency data. How did you keep it fast, for example with downsampling, virtualization, canvas/WebGL, or streaming updates? • Describe an event-driven system you worked on, for example with RabbitMQ or Kafka. How did you make ingestion idempotent? • How do you use AI coding assistants or agents in your daily work? How do you decide when to trust the output, and how do you protect sensitive data?