Software Engineering PMTS

Salesforce•San Francisco, CA

2d•$197,300 - $344,700

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. The Agentforce team owns the defensive security of Salesforce Database — the production database system behind Salesforce's cloud platform, including its storage layer, backup and restore systems, and telemetry and operational infrastructure. AI has changed the threat landscape, demanding instant response to automated adversarial attacks and requiring vulnerabilities to be addressed in hours. This role is core to building the next-generation defense system for this reality, focusing on detection, compliance enforcement, agent security guardrails, and vulnerability management that runs continuously and acts without manual review.

Requirements

Deep understanding of database security, including multi-tenant isolation, access control, and data governance.
Experience leading threat modeling and architecture risk analysis for complex distributed database cloud environments.
Expert-level design of consistent security policies across AWS and GCP, including mitigating provider-specific edge cases.
Solid grasp of distributed systems and strong programming skills (Python / Rust / Go).
Hands-on experience building or improving security detection and enforcement software for large-scale production database systems.
A track record of shipping and operating production software at scale, with a preference for direct delivery over writing specifications.
Strong written and verbal communication skills; able to lead security discussions across engineering teams and gain buy-in without formal authority.
10+ years of professional experience.

Nice To Haves

Experience with large-scale distributed databases or multi-tenant SaaS architectures is a strong plus.

Responsibilities

Own the design and hands-on implementation of security software that keeps SDB secure and compliant.
Design security detection and enforcement software for large-scale production database systems.
Build static and dynamic analysis pipelines for database vulnerability classes: SQL injection, privilege escalation, broken access control, and tenant-isolation breaches.
Apply ML to anomaly detection: data exfiltration, insider threat, and unusual access to customer data.
Build security guardrails for AI agents and LLMs operating on production databases: least-privilege configuration, scoped API access, sandbox isolation, and runtime behavioral monitoring.
Ship automation for real-time detection and response to fast, automated attacks.
Drive vulnerability management and software composition analysis across the database platform.
Partner with database engineering teams to embed security into the platform, from design review through production rollout.