Member of Technical Staff — Machine Learning & Agent Security Engineering

Job Category:
Software & Security Engineering

We’re Salesforce, the Customer Company, inspiring the future of business with AI + Data + CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world.

We are a security agentic & machine learning engineering team within the Salesforce Security organization, building scalable and resilient AI and ML capabilities for security engineering. We are looking for a hands‑on Member of Technical Staff (MTS) — Machine Learning and Agent Engineering to contribute to our platform for Security AI and automated Agentic Trust workflows. The ideal candidate is a strong Python Software and Security Engineer with practical machine learning and Agentic experience who enjoys building reliable production systems and applying emerging Agentic AI technologies to real-world engineering and security problems.

You will work within established team architectures and technical direction to deliver well‑scoped capabilities, solve implementation challenges, and operate the software you build.

• Agentic and AI Security Engineering Architect, develop, and operate high‑availability production AI and LLM agentic systems, applying tool calling, structured outputs, and state management in Python across public cloud environments. Deliver reliable, well‑tested AI services and APIs, turning emerging agentic patterns into robust software solutions.
• Scalable Security Intelligence Engineer high‑throughput data‑processing pipelines, feature workflows, and automated security intelligence systems capable of processing large‑scale security telemetry seamlessly. Operationalize machine learning models (classification, clustering, anomaly detection) to accelerate security automation and threat detection at Salesforce scale.
• Operational Ownership & Resilience Take full end‑to‑end ownership of systems across implementation, testing, deployment, and live production operations. Drive system resilience, observability, and performance through robust telemetry (logs, metrics, traces) and an attacker's mindset.

• 3+ years of professional software engineering, machine learning engineering, or related development experience.
• Strong hands‑on programming skills in Python.
• Solid software engineering fundamentals, including data structures, APIs, testing, debugging, code reviews, and maintainable software design.
• Experience building and operating production software, services, data‑processing systems, or ML applications.
• Experience solving implementation‑level challenges involving scale, performance, reliability, data volume, or concurrency.
• Practical understanding of machine learning fundamentals and experience applying ML using common libraries or frameworks.
• Familiarity with Generative AI and LLM technologies and how they can be incorporated into software applications.
• Experience working with cloud‑based, distributed, or data‑intensive applications.
• Understanding of software development practices including source control, automated testing, CI/CD, monitoring, and operational debugging.
• Ability to troubleshoot software using logs, metrics, traces, and other telemetry.
• Ability to work relatively independently within established technical direction and collaborate effectively with other engineers.
• Clear written and verbal communication skills.

• Agentic AI:
  Experience with LLM‑powered workflows, tool/function calling, structured outputs,…