RDQ327R179

While candidates in the listed location(s) are encouraged for this role, candidates in other (US) locations will be considered.

About the Team

The Agentic Security Engineering team is a horizontal, shared services engineering team that enables every Databricks security function AI-native. We enable building the AI agents, "personas," and own the 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.

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. Your work will span secure agent identity, isolation and sandboxing, policy enforcement, auditability, observability, evaluation, orchestration, and reliable integration with Databricks and security infrastructure.

You will operate across organizational boundaries, aligning engineering and security leaders on platform architecture, driving adoption of common patterns, and resolving complex technical problems that affect multiple security functions. You will influence the quality, safety, reliability, and velocity of the broader Security AI ecosystem without owning the domain logic or business outcomes of every individual persona.

The Impact You Will Have

Shared Security AI Platform Architecture

• 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.

Reliable and Safe Agentic Systems at Scale

• 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.

Cross-Organizational Technical Leadership

• Serve as a technical authority for the shared Security AI platform across Security Operations, Product Security, Governance and Risk, Compliance and Assurance, Enterprise Security, and other partner functions.
• Align engineering and security leaders on platform direction, architectural tradeoffs, adoption priorities, and standards for safe agentic systems.
• Partner with AI Champions and domain teams to turn recurring cross-functional needs into reusable platform capabilities.
• Represent the platform in architecture reviews and technical decisions involving agent identity, isolation, policy, observability, evaluation, orchestration, and secure integration.
• Drive adoption of common platform patterns and retire duplicated or inconsistent implementations across security teams.

Mentorship and Team Capability

• Mentor engineers on distributed systems, platform architecture, AI security engineering, agentic threat modeling, and technical leadership.
• Lead high-rigor design reviews and raise the bar for reliability, operability, security, and maintainability.
• Help establish a culture of measurable outcomes, disciplined evaluation, safe experimentation, and continuous improvement.
• Influence technical direction beyond the immediate team through clear written proposals, architecture reviews, and cross-functional execution.

What We Look For

• 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.

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.
• Deep understanding of emerging global AI regulatory technical requirements (EU AI Act, NIST AI RMF) and how to encode them into engineering primitives.