Software Engineer — Observability Instrumentation

The Observability Engineering Team manages access to G-Research’s telemetry platforms, ensuring our engineering teams can effectively produce and consume telemetry for their services. We are looking for a technically strong, customer-focused Software Engineer to help make observability easier to adopt across the organisation. This role focuses on the producer side: instrumentation patterns, OpenTelemetry SDKs and the collector configurations that help teams emit consistent, high-quality telemetry. This role is suited to someone who enjoys creating leverage through shared engineering patterns rather than building a single product feature set. You will use your engineering and emerging AI engineering skills to improve how teams instrument, understand and operate their services across the firm. You will help improve our service-level visibility, working closely with application and infrastructure teams enabling golden paths for instrumentation across C#, Python and Kubernetes-based services, and ongoing development of our observability capabilities. Key responsibilities Key responsibilities of the role include: Extending and maintaining OpenTelemetry SDKs, libraries and Collectors Building and supporting instrumentation paths across Kubernetes and non-Kubernetes workloads Embedding observability standards across platform and application teams Leveraging AI and AI assisted engineering to accelerate productivity and value Scaling and improving our telemetry backend systems and pipelines Improving incident response with better telemetry coverage Participating on the out of hours on-call rotation

Requirements

The ideal candidate will have the following skills and experience: Strong problem-solving skills and the ability to turn standards into usable libraries, examples and guidance Programming skills in C# and/or Python with a good understanding of software architecture Hands-on experience with OpenTelemetry SDKs, instrumentation and Collectors Proficiency in Kubernetes and cloud-native observability Good understanding of metrics, logs and traces, and how they are used by engineering teams to debug and operate services DevOps tooling experience using tools such as Terraform, ArgoCD, Helm or Jenkins Interest in AI engineering and SRE practices to improve incident response and RCA Desirable but not essential experience includes: Prometheus/PromQL, VictoriaMetrics, OpenSearch, Grafana or similar observability backends Auto-instrumentation, distributed tracing, structured logging or trace/metric correlation Kafka or telemetry pipeline architectures