At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business via general data analytics. Our goal is to make Grafana the single best place where humans and AI agents understand and act on data from across the enterprise. We build systems that help users make sense of their sea of data through AI-driven features in use cases like product analytics and sales data. These capabilities lower the barrier of domain expertise and surface meaningful signals from messy data
The 2H team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership
We’re looking for an AI Software Engineer with a strong software engineering background, a quick iteration mindset, and a passion for experimentation – balanced by a focus on shipping and scaling impactful features that deliver value to users. As part of our skunkworks initiative, you'll wear multiple hats and won't rely on cross-functional teams that typically support other teams inside Grafana Labs engineering. You will design, develop, test, and ship AI-powered features that can manage dealing with large datasets, while also expanding the capabilities of analytics-focused AI agents to assist users with information retrieval. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative
Build and deliver AI solutions: Take ownership of developing delightful, high-performance AI features to help users discover, organize, and optimize access to large datasets
Rapid experimentation and iteration: Implement a highly iterative process where you quickly prototype, test, and validate with real users, including shipping and evolving LLM- or agent-powered workflows for the data engineering lifecycle
Collaborate: Work with the rest of the team to shape AI-driven product features, including the integration of agentic components with internal tools like Slack and alerting systems while engaging with internal data teams for dogfooding
Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows
Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams
Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows