You Will

• Own the test strategy for Lynx across unit, integration, end-to-end, and non-functional testing.
• Design and build automated test suites and CI pipelines that give the team fast, trustworthy signal on every change.
• Test distributed, Kubernetes-native systems: multi-node clusters, telemetry pipelines, and services under realistic load and failure conditions.
• Develop approaches for validating ML-driven behaviour — detections, risk scores, and behavioural baselines — including regression testing, ground-truth datasets, and evaluation of false-positive/false-negative rates.
• Build tooling, fixtures, and synthetic data that let the team reproduce agent behaviour and edge cases reliably.
• Investigate, isolate, and clearly report defects, driving them to resolution with engineering.
• Serve as the quality voice in design and release decisions, and help define what "ready to ship" means for a security product.

You Have

• 5+ years in software QA or test engineering, with a strong bias toward automation over manual testing.
• Proficiency in at least one programming language used for test automation (Python preferred).
• Hands-on experience testing distributed systems, APIs, and backend services.
• Working knowledge of containers and technology like Docker
• Experience building and maintaining CI/CD pipelines and integrating automated tests into them.
• Strong debugging instincts and the ability to isolate root cause across a multi-service system
• Excellent written and verbal communication — clear bug reports, clear test plans, clear recommendations.
• Must be comfortable with, and an advocate for, AI tooling in your day-to-day workflows.

Nice to Have

• Experience testing AI-powered applications or AI/ML systems is a huge advantage! Including prompt and agent testing, LLM evaluation, RAG validation, hallucination and safety testing, regression testing of AI behaviour, benchmark creation etc
• Working knowledge of Kubernetes and comfort operating clusters for testing.
• Familiarity with observability and telemetry pipelines (metrics, logs, traces; tools such as ClickHouse, Prometheus, or similar).
• Experience validating ML or data-driven systems, or building evaluation/ground-truth datasets.
• Performance, load, or chaos testing experience.
• Exposure to eBPF, kernel telemetry, or runtime security tooling.
• Open-source contributions to testing frameworks or infrastructure projects.
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