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Posted 21 hours ago

Staff AI Engineer - Grafana AI/ML | USA | Remote

at Grafana Labs

United StatesRemote
AWSGCPAzureTerraformKubernetesDockerGrafanaSalesforce

Responsibilities

  • Build and deliver AI solutions: Take ownership of developing high-performance AI features to help users detect, triage, and resolve incidents using observability data and tools.
  • Collaborate cross-functionally: Work with data analysts, product managers, and designers to shape AI-driven product features, including integration of agentic components with internal tools, alerting systems, runbooks, and developer workflows.
  • Utilize AI tools effectively: Use AI and automation tools to enhance both product functionality and your own development workflows.

Requirements

  • With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions.
  • Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Grafana Labs to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost.
  • Staff AI Engineer
  • The Grafana AI teams play a key role in this mission by helping users make sense of complex observability data through AI-driven features.
  • What makes our team different is how we work: we operate with a high degree of autonomy and ownership, both as individuals and as a team.
  • 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.
  • You’ll work closely with cross-functional teams to develop, test, and ship AI-powered features that contribute to improving infrastructure and observability quality through automation, while also expanding the capabilities of AI agents across the observability stack to assist users with incident response.
  • 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 incident lifecycle management and automated analysis tasks.
  • 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.
  • You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company-funded usage budget so you can iterate quickly without unnecessary friction.
  • We encourage pragmatic AI-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow-ups—always paired with strong code review and quality standards.
  • You’ll also have access to frontier models (e.g., GPT-Codex 5/3, Claude Opus 4.6, Gemini 3 Pro).
  • experience building production software systems (backend and / or full stack). You’re a self-starter, capable of tackling complex engineering problems with minimal supervision. AI
  • experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory.
  • Experience with LLMs, prompt engineering, and building applications powered by GenAI.
  • Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). •
  • Experience building or working with agent frameworks or multi‑agent workflows. •
  • Experience with infrastructure / devops related tooling: Kubernetes, Docker, Terraform or similar for deployments.
  • Familiarity with model fine-tuning techniques. •
  • Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings.

Benefits

  • Experience using observability tools to understand and troubleshoot system behavior. Bonus Points For: •
  • Compensation & Rewards:
  • In the United States, the Base compensation range for this role is USD 174,986 - USD 220,000.
  • Actual compensation may vary based on level, experience, and skillset as assessed in the interview process.
  • Benefits include equity, bonus (if applicable) and other benefits listed here .
  • *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range &
  • Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable.

Contact

  • Learn more at grafana.com and follow us on LinkedIn and X .

Additional details

  • Grafana Labs, the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture.
  • Grafana Cloud, our fully managed observability platform, is flexible and built for scale.
  • We are a 100% remote company with 1,600+ team members across 40+ countries, and we’re backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P.
  • We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work.
  • Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do.
  • You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity.
  • This is a remote opportunity and we would be interested in applicants from USA time zones only at this time.
  • At Grafana, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack.
  • These capabilities reduce toil, lower the barrier of domain expertise, and surface meaningful signals from noisy environments.
  • 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.

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