Description
About the team
Auto-IAM team delivers next-generation Identity and Access Management (IAM) platform that strengthens Enterprise Security. The platform unifies security, scalability, and compliance by using AI and machine learning to enable advanced features like adaptive authentication, anomaly detection, and predictive security insights. The threat data fabric consolidates various Enterprise scale data to enable autonomous decision-making and reduce manual work.
About the team
We are seeking a talented Software Engineer to join our team in building the next-generation Enterprise Security and IAM platform. The ideal candidate has passion for designing secure, large-scale, distributed systems that serve as the backbone for autonomous security. In this role, you will develop and optimize high-performance data infrastructure, collaborate with cross-functional teams to build "Zero Trust" architectures, and contribute to the integration of intelligent, AI-driven capabilities that enable real-time threat detection and response. Curiosity and a willingness to master the intersection of hardware-backed security, distributed data fabrics, and AI is essential. If you are excited about pushing the boundaries of security by writing clean, concurrent code for advanced technologies, we encourage you to apply!
What you will be doing:
High-Performance Engineering: Design and develop scalable microservices in Golang (preferably), Python or Java.
Security Data Fabric: Build and maintain the Threat Data Fabric a unified data layer that integrates identity, device attestation (TPM/HSM), and log data for autonomous decision-making.
Infrastructure as Code: Deploy and manage global-scale containerized applications using Kubernetes, Docker, and Terraform across public cloud environments (AWS/GCP).
Enable Agentic Security: Develop the backend integration points for Retrieval-Augmented Generation (RAG), allowing security models to query real-time data with sub-millisecond latency.
Hardware-Software Synergy: Collaborate with platform teams to integrate hardware-backed security (TPM, Secure Boot) into the software data-access layer.
Observability & Integrity: Monitor and troubleshoot complex distributed systems, ensuring data integrity and proactively detecting system-level threats or misconfigurations.
Security Best Practices: Implement Zero Trust principles at the code level, including gRPC/REST API security, MFA integration, and secure token management.
What you should have:
2–4 years of professional software development experience.
Proficiency in one or more programming languages: Go, Python, Java.
Experience with distributed systems, SaaS platforms, microservices, and REST/gRPC APIs.
Familiarity with Kubernetes, Docker, Terraform, and cloud-native architectures.
Knowledge of software security best practices (e.g., OWASP Top 10, Zero Trust, MFA).
Strong problem-solving, debugging, and collaboration skills.
Knowledge and/or experience with MLOps or AI/ML concepts, with willingness to grow further in this area.
Experience working in a large-scale, enterprise environment.
Nice to have:
Familiarity with the Salesforce Einstein Trust Layer or similar AI-governance frameworks.
Experience with gRPC for low-latency inter-service communication.
Experience with IAM, Cybersecurity, or compliance frameworks (NIST, ISO, SOC 2).
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.