Role
Our first security-focused engineerTakes high-level direction (e.g., "identify top five security-related gaps for AX") and drives to resultsSuccess looks like a LanceDB platform that follows security-related best practices, and an ongoing partnership with other engineers to continuously enhance our security posture in all areas of the product
Day-to-day
Select, deploy and tune security tooling across all relevant repos and environments, ensuring full coverageIdentify and remediate security-related risks, including but not limited to cloud IAM, access controls, product vulnerabilitiesOwn vulnerability management, from running our CVE program through closely working with other engineers on specific remediationsSupport periodic evidence collection and control testing for compliance commitmentsApply relevant industry trends, best practices, and specific vulnerabilities to our product
Qualifications
8+ years as a software engineerExperience working on large-scale data platforms (e.g., databases, data infra, ML/AI systems), including work on concurrency and multitenancyExperience building encryption, authentication, authorization features for shipping products at high throughputExperience with CSP stacks, including object storage security, disk/volume encryption, key rotation, etc.Demonstrated ownership of ambiguous projects with minimal guidanceStrong written and verbal communication (can drive alignment across teams)Comfortable using data (metrics, experiments, usage) to guide decisionsExperience contributing to or working with open source communities
Please apply for this role only if you meet all of the above qualifications, and agree with the day-to-day responsibilities.
About LanceDB
LanceDB is a developer-friendly, open-source data platform for multimodal AI. From hyper-scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of large-scale datasets, LanceDB is the best foundation for your AI workloads.
Other open roles at LanceDB(3)
LanceDB is the first open-source AI-Native Multimodal Lakehouse. Build fast, reliable RAG, agents, and search engines with a multimodal vector database with native versioning and S3-compatible object storage.
Key team members
David Wang
Dave Unger
Peter Ebert
Catherine Chung
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