Sr. Staff Security Software Engineer - Agentic Security Engineering
Building and securing AI agents/agentic platforms—directly tied to agent tooling, AI safety, and platform-level AI developer experience.
About the Role
Senior Staff Security Software Engineer to architect and lead Databricks’ shared Security AI platform, enabling governed, secure agentic workflows and scalable AI personas across security teams. The role sets platform architecture, standards, and multi-year engineering direction for identity, isolation, policy enforcement, observability, and safe production operation of agentic systems.
Job Description
Role
As a Senior Staff Security Software Engineer on the Agentic Security Engineering team, you will serve as a senior technical authority and architect for Databricks’ shared Security AI platform. You will define platform architecture, technical standards, and multi-year engineering direction to enable security teams to build, deploy, and operate governed AI personas safely at scale.
Key Responsibilities
- Define and evolve the architecture for a shared Security AI platform and 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.
- Design platform support for secure multi-agent workflows, tool registries, policy-aware orchestration, and agent-to-agent collaboration.
- Lead design and development of production-grade platform capabilities enabling reliable operation of security personas across a growing AI and security surface.
- Define platform success metrics (latency, failure rates, rollback readiness, governance coverage, safety, trust, autonomy, business value) and qualification/maturity mechanisms for persona autonomy.
- Drive technical solutions for high-risk workflows involving offensive personas, untrusted inputs, sensitive data, production access, and automated tool execution.
- Operate cross-organizationally to align engineering and security leaders on platform direction, tradeoffs, and adoption priorities; represent the platform in architecture reviews and technical decisions.
- Mentor engineers, lead high-rigor design reviews, and raise the bar for reliability, operability, security, and maintainability.
Requirements
- 10+ years of progressive experience in software engineering, security platform architecture, or systems security; proven track record operating at an L7 / Senior Staff level at a major cloud, platform, or high-growth tech company.
- 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.
- Demonstrated ability to align VP/Director-level organizations on complex technical strategies and 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 and a history of raising the technical bar through design reviews, mentorship, and written architecture proposals.
Nice to Have
- 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.
- Understanding of emerging global AI regulatory technical requirements (EU AI Act, NIST AI RMF) and how to encode them into engineering primitives.
Compensation
- Zone 1: $228,600 — $314,250 USD
- Zone 2: $205,700 — $282,800 USD
- Zone 3: $194,200 — $267,100 USD
- Zone 4: $182,900 — $251,450 USD
Note: Total compensation may include annual performance bonus, equity, and region-specific benefits.
Location & Remote
- Candidates in the listed location(s) are encouraged; candidates in other U.S. locations will be considered. (No single city required by the posting.)
Benefits
- Eligibility for annual performance bonus
- Equity
- Comprehensive benefits and perks (region-specific)