Staff Site Reliability Engineer

Domino Data Lab

3h•$200,000 - $230,000•Remote

About The Position

At Domino, we build software that helps the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and novel features for collaboration, reuse, and reproducibility — all of which make data science teams more productive, reduce time to value, and ensure compliance. Our customers — like Johnson & Johnson, GSK, Bristol Myers, UBS, FINRA and the US Navy — are using our software to solve some of the most important challenges in the world, such as developing new medicines, securing our financial markets, or protecting our country. Backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake, NetApp and other leading investors, we have been in business for a decade but are still a small team operating with the spirit of a startup. Especially in the world of AI today, we believe that the future is still being invented — and we want to be the ones building it. For more information, visit www.domino.ai As our infrastructure and customer footprint grow, we're investing in a new kind of SRE practice where the people who respond to incidents also build the systems that make future incidents shorter, rarer, and less painful. We're developing AI-assisted tooling that helps our support and engineering teams diagnose problems faster, learn from outages more deeply, and automate away the toil that slows everyone down. This role sits at the center of that: equal parts hands-on operator, software engineer, and technical leader. If you believe that operational experience and engineering craft make each other stronger, you'll feel right at home here.

Requirements

Deep experience in Site Reliability Engineering, platform engineering, or a software engineering role with genuine, hands-on operational ownership
Fluency with Kubernetes, Linux, cloud platforms, and observability tooling, and the ability to use them to investigate complex, real-world production problems
A strong ability to perceive and close reliability gaps in technical products, tools and processes
Strong software engineering skills in Python or Go, with a track record of building internal tools or services that people actually rely on
Comfort leading technically ambiguous work and influencing direction across teams without needing direct authority to get things done
A history of improving reliability through engineering and automation, not just putting out fires manually
Strong communication skills and real experience mentoring engineers or shaping technical decision-making on your team
Sound judgment about AI/LLM tooling: you know where it genuinely helps in operational workflows and where it adds noise instead of signal

Nice To Haves

Experience with LLM-based systems, retrieval workflows, SaaS platform operations, or building tooling for support or developer teams

Responsibilities

Lead the development of Domino's internal AI-assisted reliability tooling, including systems that analyze tickets, logs, traces, and documentation to help teams resolve outages faster with less recurring toil
Improve the observability coverage and signal quality for our most critical customer-facing systems, so engineers have more to work with throughout the development and support lifecycle
Own incident response end-to-end, from detection to remediation, and leave each problem space better documented, better understood, and less likely to recur
Guide the development of customer and user-facing observability tools within our products
Define and mature SLO/SLI frameworks for priority services, turning abstract reliability goals into measurable, actionable standards
Scale cloud operations practices for Domino’s single-tenant SaaS offering, and work with engineering teams to improve the reliability and repeatability of customer deployments and upgrades
Mentor other engineers and shape how SRE is practiced at Domino, including incident response workflows, operational readiness expectations, and post-incident learning culture