About the job
Join Grafana Labs, a leading remote-first, open-source innovator with over 20 million users worldwide leveraging our visualization tool to monitor everything from beehives to climate change. Our distinctive dashboards have been showcased at major events, from NASA launches to the Tour de France. Serving over 3,000 companies, including Bloomberg, JPMorgan Chase, and eBay, we empower them to manage observability strategies through the Grafana LGTM Stack, which can be fully managed via Grafana Cloud or self-managed using the Grafana Enterprise Stack, both featuring scalable metrics (Grafana Mimir), logs (Grafana Loki), and traces (Grafana Tempo).
As we rapidly scale, we remain committed to our core values: an open-source legacy, a collaborative global culture, and a passion for impactful work. Our team flourishes in an innovation-driven environment where transparency, autonomy, and trust are paramount.
If you're excited about this role, don't hesitate to apply even if you don't meet every requirement—this could be a transformative step in your career.
This position is remote, and we are currently considering applicants from Canada time zones only.
Staff AI Engineer
The Opportunity:
At Grafana, we develop observability tools that enable users to understand, respond to, and enhance their systems, irrespective of scale or complexity. The Grafana AI teams are integral to this mission, utilizing AI-driven features to help users interpret complex observability data. These capabilities minimize toil, lower the domain expertise barrier, and highlight significant signals in noisy environments.
What sets our team apart is our unique approach: we emphasize autonomy and ownership at both individual and team levels. Engineers are encouraged to make decisions, rapidly prototype, and validate ideas early, all within a highly collaborative culture that values curiosity, constructive feedback, and cross-functional collaboration.
We seek an AI Software Engineer with a robust software engineering background, a mindset geared towards quick iterations, and a passion for experimentation. This should be complemented by a focus on delivering and scaling impactful features that bring real value to our users.

