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    Job · 18264590

    REMOTEFULLTIME

    Vulnerability Engineer

    Domino logo

    Domino

    Remote · remote · Posted 76d ago

    Sourced fromGreenhouse
    Not disclosedFresherfulltimeRemote

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    Section · 01

    About this role

    Who we are

    At Domino, we build solutions that help the largest, highly regulated organizations adopt AI to accelerate mission-critical use cases. Our platform integrates a streamlined model and app development environment, advanced model, agent, and app hosting  capabilities, and novel governance capabilities providing regulator-ready AI at scale. 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 and other leading investors, we have been in business for over a decade but are still a small team operating with the spirit of a startup. 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

    What we are building

    Domino's Security team safeguards a platform trusted by some of the most regulated organizations in the world, across financial services, pharma, government, and defense. Vulnerability Management is where that trust gets tested day to day: finding, triaging, and closing out risk across our OS, container, and dependency surface, and giving customers a clear, defensible answer when they ask how exposed they are. This role joins that function as it scales, working closely with our Staff Security Engineer to turn a fast-growing vulnerability workload into faster, more consistent risk assessments.

    What your impact will be

    In your first year, your impact will be:

    Faster, more consistent Vulnerability Risk Assessments. You'll own first-pass CVSS scoring and exploitability analysis, closing the SLA gap between finding and answer

    Validated, trustworthy triage. You'll reproduce and confirm customer-reported and pen-test findings before they reach Engineering, so fix priority reflects real exploitability, not just scanner severity

    Reliable scanning pipelines. You'll keep SAST/DAST and vulnerability scanning automations running, troubleshooting failures and tuning configs so the data everyone relies on stays clean

    Real partnership with Engineering. You'll build or run PoC exploits on select CVEs, bringing validated risk, not just findings, into prioritization conversations

    More capacity for the function. You'll free up our Staff Security Engineer to focus on program-level improvement instead of carrying all of vulnerability management's day-to-day load

    What we look for in this role

    Hands-on experience managing vulnerabilities for a large SaaS product, across OS, container, and dependency exposure

    A track record triaging and tracking CVEs for a SaaS or containerized product: reading scan reports, prioritizing by severity, and following through to resolution

    Experience reproducing and validating reported vulnerabilities, whether from customer disclosures or pen test findings, not just logging them

    Time spent with vulnerability scanning tools such as Prisma Cloud/Twistlock, JFrog, or Trivy, including reconciling findings across tools

    Comfort building or maintaining SAST/DAST pipeline automation, and triaging what the scans turn up

    Experience partnering with Engineering to get fixes prioritized and shipped, not just reported

    Background in a highly regulated environment or modern software company, ideally one that moves at startup or scale-up speed

    Strong scripting ability, Python preferred

    Working knowledge of CVSS v3.1/v4.0 scoring and the judgment to assess risk, not just report it

    Exploit development or PoC skills to validate real-world exploitability of CVEs, using tools like Burp Suite

    Familiarity with OWASP Top 10 and testing methodology

    Working knowledge of containers and Kubernetes, plus core Linux, AWS, and networking fundamentals

    Basic understanding of authentication/authorization concepts (tokens, session handling, auth bypass patterns) and API security fundamentals

    Basic threat modeling: thinking in attack paths, not just isolated severity scores

    Clear communication, comfortable navigating risk conversations with Engineering and customers, including drafting risk statements a non-technical audience will actually read

    Comfort operating with ambiguity, since not every finding arrives with a clean severity or fix path

    Nice to have:

    OSWA, OSWE, or a similar offensive security certification (e.g. GWAPT, GPEN)

    Familiarity with Airflow and Snowflake

    What we value

    We value a growth mindset. High-performing creative individuals who dig into problems and see the opportunities for success

    We believe in individuals who seek truth and speak the truth and can be their whole selves at work

    We value all of you that believe improving is always possible At Domino Everything is a work in progress – we can do better at everything

    We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company

    We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply

    #LI-Remote

    Sourced from greenhouse

    Section · 02

    Skills

    Kubernetesamazon web servicesapache airflowPythonSnowflakeLinux

    Section · Company

    About Domino Data Lab

    Domino logo

    Domino

    Internet

    2013

    13 years old

    San Francisco,California

    United States

    About

    Domino Data Lab provides an enterprise platform designed to help organizations build, deploy, and manage AI and machine learning models. Domino Data Lab provides an enterprise platform designed to help organizations build, deploy, and manage artificial intelligence and machine learning models. The platform supports collaboration across data science, IT, and business teams, enabling the development of models at scale while maintaining governance and reproducibility. It offers tools for model monitoring, experiment tracking, and workflow automation, ensuring that models remain accurate and compliant throughout their lifecycle. Domino Data Lab also integrates with a wide range of data science tools and infrastructure, giving teams flexibility in their work. By streamlining processes from experimentation to production, the company helps enterprises accelerate data-driven decision-making.

    Industries

    Internet

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