
201 - 500 employees
Founded 2013
🤖 Artificial Intelligence
🏢 Enterprise
☁️ SaaS
💰 Series F on 2022-06
Artificial Intelligence • Enterprise • SaaS
Domino Data Lab is a company that empowers AI-driven enterprises to build and manage AI at scale through its Enterprise AI Platform. The platform provides an integrated experience for model development, MLOps, collaboration, and governance, enabling global enterprises to innovate across various sectors. Domino supports better medicinal development, productive agriculture, and competitive product creation. Established in 2013 and backed by notable investors like Sequoia Capital and NVIDIA, Domino enables companies to optimize AI deployment effectively.
🕒 August 12
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201 - 500 employees
Founded 2013
🤖 Artificial Intelligence
🏢 Enterprise
☁️ SaaS
💰 Series F on 2022-06
Artificial Intelligence • Enterprise • SaaS
Domino Data Lab is a company that empowers AI-driven enterprises to build and manage AI at scale through its Enterprise AI Platform. The platform provides an integrated experience for model development, MLOps, collaboration, and governance, enabling global enterprises to innovate across various sectors. Domino supports better medicinal development, productive agriculture, and competitive product creation. Established in 2013 and backed by notable investors like Sequoia Capital and NVIDIA, Domino enables companies to optimize AI deployment effectively.
• Build and maintain a hardened release pipeline producing container images with strict scan thresholds, reproducible builds, and minimal attack surface • Build automated vulnerability triage that ingests scanner output, deduplicates findings, routes findings to owners, and tracks SLA compliance • Develop provenance and exploitability tooling generating SBOMs and VEX statements on every release • Establish a scalable remediation practice using AI and automation to manage vulnerability findings • Own the hardened image pipeline for demanding deployments and reduce remediation time for critical and high findings to published SLAs • Replace one-off vulnerability firefighting with an automated path from scan to fix • Document evidence-backed engineering decisions for vulnerabilities that will not be fixed • Set technical direction and tooling for future team development
• Experience shipping and maintaining software or container images into regulated, on-premises, or air-gapped environments • Deep fluency with container build systems, base image strategy, and reproducible builds • Working knowledge of Kubernetes and Helm packaging • Hands-on experience with vulnerability scanning and provenance tooling • Ability to distinguish real security risks from scanner noise • Strong Go and/or Python skills for automation and patching upstream open-source dependencies • Clear technical writing for external auditors and customer security teams • Bias toward removing whole classes of problems rather than closing individual tickets
• Growth mindset and opportunities for success • Environment of teaching and learning • Tools needed to be successful in the role and company • Diverse and inclusive workplace
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