Sr. Staff Security Software Engineer - Agentic Security Engineering
California · Virginia · 10+ years experience · Remote · Posted Oct 13, 2026
At a glance
- Experience
- 10+ years experience
- Workplace
- Remote
- Location
- California · Virginia
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.
About the role
- As a Senior Staff Security Software Engineer on the Agentic Security Engineering team, you will be a senior technical authority and architect for Databricks’ shared Security AI platform.
- You will define the platform architecture, technical standards, and multi-year engineering direction that allow security teams to build, deploy, and operate governed AI personas safely at scale.
Responsibilities
- Define and evolve the architecture for the shared Security AI platform and its paved road for governed agentic workflows.
- Establish reusable patterns for agent identity, least-privilege access, sandboxing, egress control, secret isolation, policy enforcement, auditability, and rollback.
- Set engineering standards for reliability, observability, evaluation, incident response, cost instrumentation, and safe production deployment.
- Make high-leverage build, buy, and adopt decisions that keep the platform focused on security-specific capabilities while using Databricks platform primitives wherever they meet the need.
- Design the platform to support secure multi-agent workflows, tool registries, policy-aware orchestration, and agent-to-agent collaboration.
- Lead the design and development of production-grade platform capabilities that enable security personas to operate reliably across a growing AI and security surface.
- Define how the platform measures successful runs, latency, failure rates, rollback readiness, safety, governance coverage, trust, autonomy, and business value.
- Establish qualification and maturity mechanisms that determine what a security persona may do, what controls it requires, and how it earns additional autonomy.
- Drive technical solutions for high-risk workflows, including offensive security personas, untrusted inputs, sensitive data, production access, and automated tool execution.
- Improve the velocity of safe AI development without compromising security, governance, or operational reliability.
Requirements
- 10+ years of progressive experience in software engineering, security platform architecture, or systems security, with a proven track record of operating at an L7/Senior Staff level at a major cloud, platform, or high-growth tech company.
- Production Systems Engineering: Exceptional fluency in Python and modern cloud-native systems architecture (Kubernetes, distributed streaming/storage, high-throughput microservices) with experience delivering fault-tolerant, low-latency security infrastructure.
- Multi-Org Architectural Execution: Demonstrated ability to align multiple VP/Engineering Director-level organizations on complex technical strategies and successfully ship multi-quarter architectural transformations.
- Deep practical expertise in AI/ML security, agentic system trust boundaries, prompt injection and tool-use risks, adversarial testing, identity and authorization, data protection, and secure deployment of AI-enabled systems.
- Experience designing platform guardrails for sensitive workflows involving untrusted input, privileged tools, secrets, production systems, or confidential data.
- Strong technical communication skills, including the ability to explain complex platform, security, and reliability tradeoffs to engineers, security leaders, and senior executives.
- A track record of raising the technical bar through design reviews, mentorship, written architecture proposals, and influence beyond formal reporting lines.
Preferred qualifications
- Experience building secure agent platforms, developer platforms, security control planes, or internal infrastructure used by multiple teams.
- Experience with MLOps, AI serving infrastructure, security data platforms, detection systems, or large-scale security automation.
- Published research, patents, or recognized contributions in adversarial ML, AI safety frameworks, or open-source security platforms.
- Hands-on experience building or securing large-scale LLM training pipelines, vector databases, or AI agent runtime environments.
- Deep understanding of emerging global AI regulatory technical requirements (EU AI Act, NIST AI RMF) and how to encode them into engineering primitives.
Benefits
- annual performance bonus
- equity
- comprehensive benefits and perks
Skills
- Python
- Kubernetes
- Distributed streaming/storage
- Microservices
- AI/ML Security
- LLM
- Vector Databases
- MLOps
