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

Grafana Labs

12d•$174,986 - $220,000•Remote

About The Position

Grafana Labs is seeking a Staff AI Engineer with a strong software engineering background and a passion for experimentation to join their AI/ML team. This role is remote and focused on developing AI-powered features that help users understand, respond to, and improve their systems using observability data. The engineer will work on building and shipping impactful features, including LLM- or agent-powered workflows for incident lifecycle management and automated analysis tasks. The team operates with a high degree of autonomy and ownership, encouraging rapid iteration, experimentation, and collaboration with cross-functional teams. The role offers significant opportunity for growth and impact, with access to modern AI tools and frontier models.

Requirements

Experience with LLMs, prompt engineering, and building applications powered by GenAI.
Proven track record of delivering software that made it into production and is actively used by users.
Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure).
Experience using observability tools to understand and troubleshoot system behavior.
Strong engineering skills: Solid 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.
Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset.
Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined.
Collaborative attitude: You communicate effectively with peers, product managers, and designers. You’re open to feedback, and you bring a solutions-oriented mindset to the table.

Nice To Haves

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.
Experience building observability tooling.

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.
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.
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.
Effective communication: Communicate effectively and contribute across teams in a highly dynamic and collaborative environment.
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.