Member of Technical Staff - Machine Learning & Agent Security Engineering

salesforce.com, inc.; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Salesforce is seeking a hands-on Member of Technical Staff in Machine Learning and Agent Security Engineering to build scalable AI and security capabilities. You will work on high-availability AI/LLM agentic systems, applied in cloud environments, with a focus on reliability and security.

You will own end-to-end lifecycle for production ML services, contribute to platform security workflows, and apply agentic AI techniques to real-world engineering challenges, collaborating with a

Qualifications

• 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.

Responsibilities

• Architect, develop, and operate high-availability AI and LLM agentic systems, applying tool calling, structured outputs, and state management in Python across public cloud environments.
• Engineer high-throughput data-processing pipelines, feature workflows, and automated security intelligence systems capable of processing large-scale security telemetry seamlessly.
• 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.

Skills

Python

Software engineering

ML fundamentals

Production software

Scale & performance

Job description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

Member of Technical Staff - Machine Learning & Agent Security Engineering
Job Category: Software & Security Engineering

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

About the Team

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.

Your Impact

1. 1. 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.
2. 2. 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.
3. 3. 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.

Required Qualifications

• 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.