Software Verification Engineer

We are building the next generation of verification infrastructure for complex networking and distributed systems. We are looking for a software engineer with strong hands-on experience in test automation and system verification, combined with practical experience applying AI to engineering workflows.

This is a hands-on verification role. You will validate product features, develop automated tests, maintain CI and regression coverage, and build deep domain expertise in networking and telemetry. In parallel, you will build agentic workflows that understand product changes, plan coverage, generate and execute tests, analyze failures, and provide engineers with trustworthy, actionable results.

What you'll be doing

• Own the end-to-end verification of product features, including requirements analysis, risk assessment, test planning, system-level validation, automated test development, execution, and release readiness.
• Build and maintain Python-based test infrastructure, CI pipelines, and regression suites, continuously improving coverage, reliability, execution time, and the quality of engineering feedback.
• Develop deep domain expertise in networking, telemetry, and distributed systems, and use it to investigate complex failures across the product, test automation, and lab environment.
• Design and build agentic verification workflows that analyze product and code changes, plan test coverage, select or generate tests, execute them, observe results, and perform controlled diagnosis and correction loops.
• Turn expert verification practices into repeatable and measurable workflows, integrating agents with source control, requirements, CI/CD, issue tracking, lab orchestration, and observability systems while defining test oracles, guardrails, human approval points, evaluation, and tracing.
  
  

• B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
• 3+ years of software engineering experience, with significant hands-on work in test automation, system verification, test infrastructure, or related areas.
• Deep, production-grade Python expertise, including type-safe data modeling, clear APIs and tool contracts, automated testing, static analysis, performance-aware implementation, and mechanisms for validating and monitoring AI-generated code.
• Experience testing complex product features and building production-quality automation, CI pipelines, and regression environments, together with strong debugging skills and knowledge of Linux, Git, APIs, containers, and modern software engineering practices.
• Hands-on experience building reliable LLM-based or agentic workflows beyond the prototype stage, including tool integration, code generation or modification, execution and feedback loops, controlled corrective actions, evaluation, guardrails, observability, context management, and prompt or model versioning.
  
  

• Experience with networking, telemetry, distributed systems, data center infrastructure, or hardware and software integration.
• Experience building system-level verification frameworks, large regression environments, test labs, topology orchestration, or traffic-generation systems.
• Experience validating high-scale data ingestion, observability pipelines, metrics, logs, traces, or other telemetry-intensive systems.
• Experience with agent-driven change-impact analysis, test selection, failure clustering, root-cause analysis, automated debugging, or flaky-test management.
• A track record of turning engineering or AI prototypes into reliable capabilities adopted by multiple engineers or teams, combined with a strong systems mindset and a practical approach to establishing correctness.