Member of Technical Staff, Intern

Toronto, ON
Hybrid
Posted Aug 14, 2026
1 position

Opens an external site

Employment type: Internship / apprenticeship
Experience level: Entry, Junior · 0+ years
Posting language: English
Working hours: 40 hours per week

Job summary

You will manage the SRE Skills Bench open-source project by designing evaluation scenarios and expanding datasets for AI reliability testing. Additionally, you will collaborate with researchers and engineers to analyze results and promote the benchmark within the AI and reliability communities.

Job details

ABOUT ROOTLY At Rootly [https://rootly.com/], we are on a mission to be the go-to way companies respond when things go wrong, helping every organization be more reliable. We do this by building an industry-leading incident management platform that allows companies around the world to consistently and quickly resolve incidents. We are not simply transforming an industry, we are carving out a new multi-billion-dollar category ourselves and need incredible talent to achieve this ambitious goal together. Customers love Rootly. Some of the fastest-growing companies around the world such as NVIDIA, DoorDash, Uber, Canva, Tripadvisor, Squarespace and more rely on Rootly to power their critical incident management process. They obsess over our delightful enterprise-ready platform and unique partnership model. See why our customers have reviewed us 5 stars on G2 [https://www.g2.com/products/rootly-manage-incidents-on-slack/reviews]. Investors love Rootly. We are backed by some of the most respected funds in the world from Y Combinator to operators like the CTO of Dropbox and GitHub. We'd be happy to disclose our entire funding and profitability picture live during the interview. As a culture we relentlessly put transparency first. We conduct monthly financial reviews as a team so everyone has a pulse on the health of the business and publish what we are building in our weekly changelog [https://rootly.com/changelog]. ABOUT ROOTLY AI LABS Rootly AI Labs is a fellow-led research and open-source team exploring how AI can make complex systems more reliable and the people operating them more effective. We build benchmarks, prototypes, and practical tools that test frontier models against real reliability challenges, from incident response and on-call health to agent behaviour under pressure. We work in public, move quickly from ideas to experiments, and share what we learn with the broader engineering community. Our goal is to turn promising AI research into useful, trustworthy systems while keeping human judgement at the centre. Work from Rootly AI Labs has been presented at NeurIPS, ACL, ICML, and Meta's AI Ops Conference. Rootly AI Labs brings together researchers, operators, and builders from across the industry, with support from AWS, Google DeepMind, Anthropic, and Zalando. ABOUT THE ROLE You will manage SRE Skills Bench, Rootly AI Labs' open-source benchmark for measuring how well AI models handle real-world reliability engineering tasks. You will help move the project from research questions to public releases. This includes designing realistic SRE scenarios, expanding the evaluation dataset, running model evaluations, improving the benchmark infrastructure, and sharing the findings with the AI and reliability communities. The role combines applied AI research, technical project management, community building, and public communication. You will report directly to the Head of Rootly AI Labs and work closely with fellows, researchers, and engineers across the community. WHAT YOU'LL DO * Help shape the roadmap and research questions for SRE Skills Bench. * Help build an active community around the benchmark and encourage outside contributions. * Create realistic evaluation tasks based on incident response, infrastructure, debugging, observability, and production operations. * Help develop reliable and reproducible methods for evaluating frontier and open-source models. * Analyze results and turn them into reports, technical articles, and public datasets. * Collaborate with SREs, researchers, model providers, and open-source contributors. * Evaluate how new models, agents, and coding environments perform on operational work. * Help establish SRE Skills Bench as the industry standard for evaluating AI on reliability engineering. WHAT YOU'LL NEED * Hands-on experience or a strong interest in SRE, infrastructure, platform engineering, DevOps, or production operations. * Familiarity with LLM evaluations, benchmarks, agents, or applied AI research. * An experimental mindset, with an interest in designing reproducible tests, examining imperfect results, and learning quickly. * A self-driven approach, with the initiative to make sound decisions and move ambitious projects forward with minimal guidance. * The ability to explain technical work clearly and enthusiasm for sharing it through articles, social media, and talks at meetups or conferences. * Comfort working in a hybrid environment and collaborating asynchronously with a curious, distributed team. ROLE DETAILS Employment type: Full-time internship. Duration: Ongoing internship, with the possibility of conversion to a full-time role based on fit and project needs. This is a newly created vacancy. As we build out the team, we may use AI to support parts of our interview process, like reviewing assessments, but it's a tool that helps our people, not a substitute for their judgement. If this sounds like the kind of challenge and opportunity you're looking for, apply now and let's build something great together. Accommodations are available on request at any stage of our recruitment process. Contact recruitment@rootly.com and we will work with you to meet your needs confidentially. Rootly is an equal opportunity employer. We do not discriminate on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, record of offences, marital status, family status, disability, or any other ground protected by the Ontario Human Rights Code or applicable law.

What you’ll do

Requirements

Candidates should have hands-on experience or strong interest in SRE, infrastructure, or DevOps, along with familiarity with LLM evaluations and AI research. You must possess an experimental mindset, strong self-driven initiative, and the ability to communicate technical findings clearly to a broader audience.