Member of Technical Staff - Machine Learning & Agent Security Engineering

Salesforce; Washington; On-site; USD 150,000 - 190,000; Full time

Job summary

Salesforce is seeking a hands-on Member of Technical Staff in Machine Learning & Agent Security Engineering to contribute to our AI security platform. You will develop scalable AI services, apply agentic ML techniques, and own production systems from development through live operation.

The ideal candidate has strong Python software and security engineering background, experience with ML frameworks, and the ability to work across cloud environments to deliver reliable, scalable solutions.

Qualifications

• 3+ years of professional software or ML engineering experience.
• Strong Python programming skills.
• Experience building and operating production software, services, data-processing systems, or ML applications.
• Familiarity with cloud-based, distributed systems.
• Ability to work relatively independently within established technical direction.

Responsibilities

• Architect, develop, and operate high-availability production AI and agentic systems, applying tool calling and state management in Python.
• Engineer high-throughput data-processing pipelines and automated security intelligence systems at Salesforce scale.
• Take end-to-end ownership of systems from implementation to live production operations, with strong telemetry and resilience.

Skills

Python

Machine Learning

API design

Testing

CI/CD

Cloud computing

Data processing

Concurrency

Debugging

Documentation

Tools

PyTorch

scikit-learn

Hugging Face

XGBoost

Job description

Job Category

Software Engineering

Job Details

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.

Member of Technical Staff - Machine Learning & Agent Security Engineering

Job Category: Software & Security Engineering

About Salesforce

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.

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. 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. 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. 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.
• 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.

Preferred Qualifications

Experience in one or more of the following areas is helpful but not required:

• Agentic AI: Experience with LLM-powered workflows, tool/function calling, structured outputs, context/state management, or multi-step automated workflows.
• ML Frameworks: Experience with PyTorch, scikit-learn, Hugging Face, XGBoost, or similar ML frameworks.
• Distributed & Data Processing: Experience with technologies such as Ray, Spark/PySpark, Kafka, Flink, Airflow, or equivalent technologies.
• Cloud & Conta