Security Data Science Engineer

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Salesforce is the world's #1 AI CRM platform. Our Security Engineering organization plays a critical role in building intelligent, data-driven and AI-powered systems that enable real-time threat detection, automated attack response, and proactive defense across Salesforce's global infrastructure. We develop platforms that combine security domain expertise with advanced data science and AI agent capabilities to transform large-scale security telemetry into actionable detections, decisions, and automated actions.

Position Overview:
We are seeking a talented and motivated Security Data Scientist / ML Engineer with experience deploying, monitoring, and maintaining machine learning systems in security domains to join our Security Engineering organization. In this role, you will collaborate closely with security engineers, product teams, and internal stakeholders to understand high-impact security problems, design detection and response solutions, and contribute to the development, release, and operation of models and AI agents that protect Salesforce at scale. As a senior technical contributor, you will directly impact the effectiveness of Salesforce's real-time threat detection and automated defense capabilities.

You will also play a key role in advancing our use of large language models (LLMs) and AI agents to accelerate security insight generation and response. By integrating LLMs with security telemetry and detection pipelines, you will help automate root cause analysis, generate contextual explanations, and enable autonomous or semi-autonomous security actions.

Responsibilities:

Design and Develop AI-Driven Defense Systems: Build and deploy intelligent, data-driven systems that utilize machine learning and AI agents to enable real-time attack pattern identification, risk assessment, and proactive defense across Salesforce products and global infrastructure.

Advance Agentic AI & LLM Capabilities: Lead the integration of Large Language Models (LLMs) and autonomous agents with security data pipelines. Focus on automating root cause analysis, generating contextual explanations for risks, and enabling autonomous or semi-autonomous security actions.

Operationalize Security Models: Oversee the end-to-end lifecycle-including development, release, monitoring, and operation-of ML models and AI agents to ensure they deliver high-impact protection at scale.

Automate Attack Simulation: Develop autonomous AI agents capable of executing complex security tasks, such as performing advanced data correlations, identifying specific attack patterns, conducting automated penetration testing, and analyzing security impacts.

Transform Telemetry into Action: Engineer platforms that synthesize large-scale security telemetry into actionable risk intelligence and automated decisions.

Cross-Functional Collaboration: Partner closely with security engineers, infrastructure teams, and product stakeholders to deeply understand the threat landscape and design solutions that address high-priority security problems.

Apply LLMs and prompt engineering to automate generation of security insights, explanations, and response workflows from detections and anomalies
Continuously improve algorithmic performance with a focus on detection, classification, and behavioral modeling in security and threat intelligence domains
Collaborate effectively with team members and suggest improvements to reduce time-to-detection and mature our security ML platform

Qualifications:

• Professional Experience: 6+ years of industry experience with a demonstrated passion for crafting, analyzing, and deploying scalable machine learning solutions.

• Security Domain Expertise: Proven track record in machine learning engineering focused on security use cases, such as anomaly detection, malware classification, or behavioral modeling.

• ML Operations (MLOps) & Cloud:

  • Experience deploying, monitoring, and maintaining ML systems in cloud environments (specifically AWS or GCP).

  • Consistent record of building ML products using modern lifecycle methodologies, including CI/CD, QA, and Agile practices.

• Technical Proficiency:

  • Coding: Expert-level proficiency in writing high-quality, well-documented, and tested code, with a strong preference for Python.

  • Data Engineering: Solid understanding of data transformations and analytics using libraries and languages such as Pandas, Scikit-learn, SQL, and Spark.

  • Tooling: Hands-on experience with standard ML and orchestration tools like mlFlow, Airflow, and Docker.

• Foundational Knowledge: Strong understanding of Statistics and Machine Learning methods, including the challenges associated with the end-to-end ML project lifecycle.

• Agentic AI & LLM Expertise: Familiarity with Large Language Model (LLM) applications in security, including prompt engineering and the orchestration of AI agent workflows. The ability to not just build models but also define their strategic application in business workflows is highly valued.

• Collaborative Leadership: Experience working within teams managing mature data science or ML-driven security products.

• Communication: Excellent communication skills with the ability to translate complex technical concepts for internal stakeholders and product teams.

Unleash Your Potential

When you join Salesforce, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we'll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what's possible - for yourself, for AI, and the world.

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