About the job
About Rootly
At Rootly, we are dedicated to becoming the preferred solution for organizations when incidents occur, enhancing reliability across the board. Our industry-leading incident management platform empowers companies globally to swiftly and effectively address incidents. We are not just revolutionizing an industry; we are pioneering a brand-new, multi-billion dollar segment and require exceptional talent to help us realize this ambitious vision.
Our clients love Rootly. Fast-growing enterprises such as NVIDIA, Figma, Canva, Tripadvisor, and Squarespace depend on Rootly to streamline their critical incident management processes. They appreciate our user-friendly, enterprise-ready platform and our unique partnership model. Discover why we have earned 5-star reviews on G2.
Our investors share our passion. Backed by esteemed funds including Y Combinator and prominent operators like the CTOs of Dropbox and GitHub, we are eager to share our comprehensive funding and profitability status during interviews. Transparency is integral to our culture; we conduct monthly financial reviews as a team to keep everyone informed about the company's health and share updates via our weekly changelog.
About the Role
We are seeking a Senior AI Engineer to spearhead the development of an innovative AI-driven reliability assistant. This role transcends traditional machine learning tasks; it focuses on LLM engineering and AI agent development, utilizing cutting-edge AI technologies (including LLMs, vector embeddings, semantic and hybrid search, and vector databases) to create a non-deterministic product that delivers significant value to engineers and accelerates incident resolution.
As an early member of the engineering team at Rootly, you will have complete ownership of this initiative, playing a crucial part in shaping both the product and the company. If you excel in a dynamic environment that values rapid development, deployment, and iteration, this opportunity is perfect for you.
What You'll Do
- Lead the creation of an AI-powered reliability assistant that extracts pertinent context from various data sources such as observability tools, GitHub, deployment histories, past incidents, and playbooks.
- Develop LLM-powered features that convert raw data into actionable insights for engineers managing incidents.
- Establish evaluation frameworks to assess AI performance in real-world scenarios.

