Member of Technical Staff — Machine Learning & Agent Security Engineering

Salesforce•San Francisco, CA

6d•$117,200 - $194,200•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

Experience with LLM-powered workflows, tool/function calling, structured outputs, context/state management, or multi-step automated workflows.
Experience with PyTorch, scikit-learn, Hugging Face, XGBoost, or similar ML frameworks.
Experience with technologies such as Ray, Spark/PySpark, Kafka, Flink, Airflow, or equivalent technologies.
Experience with Docker, Kubernetes, or cloud-based ML/data infrastructure.
Experience deploying, evaluating, monitoring, or operating ML models and AI applications.
Familiarity with cybersecurity concepts, security engineering, security telemetry, threat detection, or security operations.
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.

Responsibilities

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.
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.
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.