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San Francisco, California, United States
Remote only
RDQ226R605
This role can be based remotely anywhere in the United States.
Databricks is the data and AI company. More than 12,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.
The Agentic Security Engineering team is a horizontal, shared services engineering team that makes every Databricks security function AI-native — building the AI agents, "personas," and shared platform that security teams across the org (Detection & Response, Threat Intelligence, Vulnerability & Product Security, Red Team, GRC, and Continuous Monitoring) build on to operate at machine speed, safely and reliably.
We leverage AI to transform how security is done.
As Staff Security Software Engineer, you own a major technical workstream designing and shipping production-grade AI agents and the platform capabilities that power them, with reliability, observability, and safe-by-default security as first-class requirements.
You set the standards for how agents are built and operated and co-build with partner security teams. Example use cases you'll enable, led by AI threat detection at scale (alert triage and false-positive reduction, fleet-scale anomaly detection), extend to threat hunting, vulnerability management, product security, red team/offensive testing, and GRC/assurance use cases.
Design, ship, and operate production AI agents/personas that security teams depend on, starting with AI threat detection at scale.
Build and harden the shared platform so any security team can ship agents safely and reliably — i.e., sandboxing, scoped least-privilege identity, observability, and a reusable agent/tool catalog.
Set engineering standards and make reliability first-class — design review bar, latency/cost optimization, monitoring, clean CI-based deploys, and safe model upgrades.
Establish AI quality and evaluation practices: eval frameworks, LLM-as-judge (with bias controls), and regression gates that catch quality drops before production.
Lead the design and development of AI platform capabilities that operate at production scale — behavioral analysis of usage, detection of prompt injection attempts, and anomaly detection on agentic workflow behavior.
Define the methodology for AI security assessment: how Databricks systematically evaluates new AI capabilities against a comprehensive threat model before deployment and monitors them continuously after.
Drive technical strategy for AI red-teaming tooling: automated adversarial testing platforms that simulate how real attackers attempt to abuse Databricks' AI systems.
Co-build with and enable partner teams across the security org, and mentor mid-level engineers.
Lead design reviews, define team engineering practices, and drive continuous improvement in the quality and reliability of AI security tooling.
Demonstrated experience building, shipping, and operating production AI agents that others depend on, with an intentional pivot to AI-native transformation of a function at scale (prototypes and demos don't count).
Expert-level Python for production systems at scale.
Depth across the production-AI dimensions: agent architecture & orchestration; context & memory management; production operations (latency, cost, caching, monitoring, model upgrades, reliability); and AI quality & evaluation (eval frameworks, LLM-as-judge, regression testing).
Hands-on experience with modern agentic frameworks and LLM tooling (e.g., LangChain, Hugging Face, frontier models) and MCP-style tool integration.
Strong technical communicator who aligns partners and drives adoption without direct authority.
7+ years in software or security engineering, with demonstrated technical leadership on a significant workstream.
Track record of shipping high-quality security tooling that other teams depend on in production.
Research contributions or deep familiarity with adversarial ML, AI safety, or AI red-teaming methodology.
Experience with MLOps platforms, AI serving infrastructure, or AI platform security at cloud scale.
Familiarity with AI governance standards (NIST AI RMF, ISO/IEC 42001, EU AI Act technical provisions) as they apply to security engineering.
Open-source contributions or publications in AI security, adversarial ML, or security tooling.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected base salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipated utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
