Member of Technical Staff — Machine Learning & Agent Security Engineering
Salesforce
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Job summary
Salesforce is seeking a hands-on Member of Technical Staff for Machine Learning & Agent Security Engineering. You will architect, develop, and operate high-availability AI/LLM agentic systems and scalable security intelligence tools.
You will own end-to-end development, deployment, and production operations, delivering reliable AI services while advancing agentic workflows and security capabilities at Salesforce scale.
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.
Responsibilities
- Architect, develop, and operate high-availability production AI and LLM agentic systems across cloud environments.
- Deliver reliable, well-tested AI services and APIs for agentic workflows.
- Engineer high-throughput data-processing pipelines and automated security intelligence systems.
- Operationalize ML models to accelerate security automation and threat detection at scale.
- Take end-to-end ownership of systems from implementation to live production operations.
- Drive system resilience, observability, and performance through telemetry and robust design.
Skills
Tools
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.
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.
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.
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.
- 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.
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.
- 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
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