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H builds computer-use agents that take actions in browsers and desktop applications. You will own observability strategy, technical design, and rollout across the entire company stack, providing visibility that enables key decision-making and progress tracking.
This is a hands-on lead role where you set direction, design the observability stack, lead technical implementation, and drive adoption across internal teams. You will ensure teams measure what matters and develop deep knowledge of H's products and services.
Key responsibilities:
- Establish a unified observability strategy applicable across teams and technical stacks
- Own implementation of systems managing metrics, logs, traces, alerts, and dashboards
- Define SLAs, SLOs, and SLIs in collaboration with product, research, and infrastructure teams
- Build end-to-end visibility of system dependencies with visualizations for users and context for agents
- Automate tracking of coverage, reliability, alert quality, and execution costs
First-year milestones:
- Month 3: All H products have SLAs, SLOs, and SLIs defined and measured in code with automated compliance reporting
- Month 6: Single complete solution covering metric collection, log processing, and tracing
- Month 12: Every system at H uses consistent SDKs and interfaces for alarms, metrics, logs, and traces
You will collaborate with infrastructure, product, engineering, and research teams. The VP of Engineering is your hiring manager.
The role is hybrid with three days per week in either the Paris or London office, plus occasional travel between locations.
Requirements:
- Demonstrated experience setting direction for substantial engineering work, delivering it, and securing team adoption
- Production experience building instrumentation and telemetry integrations
- Experience defining both product-level and technical-level SLAs, SLOs, and SLIs
- Familiarity with SRE measurements including error budgets, burn rates, MTTA/MTTR/MTTM
- Experience building self-managed observability systems such as OTEL, Prometheus, Grafana, Loki, Mimir, Alloy, Elasticsearch, or Kibana
- Experience working with Kubernetes at scale, enterprise multi-tenancy, inference, or asynchronous agent workflows
- AI experience is not required; strong match on most criteria is sufficient
