Member of Technical Staff — Machine Learning & Agent Security Engineering

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 & Containers

Experience with Docker, Kubernetes, or cloud-based ML/data infrastructure.

MLOps

Experience deploying, evaluating, monitoring, or operating ML models and AI applications.

Security Domain Expertise

Familiarity with cybersecurity concepts, security engineering, security telemetry, threat detection, or security operations.

Adversarial AI

Exposure to adversarial AI/ML, AI red teaming, LLM/agent security, attack simulation, or automated security evaluation. Familiarity with security frameworks such as MITRE ATT&CK or OCSF.

What Success Looks Like

A successful MTS on this team:

Consistently delivers well-scoped engineering work with high quality. Writes clean, tested, maintainable production Python. Understands the designs and architecture relevant to the features they work on.

Works relatively independently once technical direction is established. Solves implementation challenges without requiring detailed step-by-step direction. Builds software that operates reliably beyond prototype or notebook environments.

Can implement functionality that needs to operate across meaningful data volumes, workloads, or concurrent executions. Understands how their implementation behaves under production constraints and failure conditions. Applies ML and modern AI technologies to engineering problems.

Uses telemetry to debug common production issues and improve system reliability. Owns their work through implementation, testing, deployment, monitoring, and production operation. Knows when an implementation decision can be made independently and when to involve senior engineers in broader design decisions.

Contributes effectively to code reviews, design discussions, documentation, and team execution. Continues developing expertise in ML, Generative AI, agentic workflows, and the security domain.