Lead Member of Technical Staff; LMTS — Security Data Science & ML Engineering

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Software Engineering

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.

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Salesforce is the world’s #1 AI CRM, where humans and intelligent agents work together to drive customer success. As the company leading workforce transformation in the agentic era, Salesforce is redefining how data, AI, and trust converge at global scale.

Within Salesforce, the Security Engineering organization builds intelligent, data-driven, and AI-powered platforms that protect our global infrastructure and customers. We transform massive volumes of security telemetry into real-time detections, decisions, and automated defensive actions, combining deep security domain expertise with modern data science, machine learning, and agentic AI systems.

We are seeking a hands‑on Lead Member of Technical Staff (LMTS) who blends software engineering rigor, data engineering depth, and production‑grade machine learning to power agentic security experiences.

This role sits at the intersection of high-throughput data platforms, ML‑driven risk intelligence, and autonomous decisioning systems. You will design, build, and operate scalable data and ML services that enable real‑time threat detection, automated response, and proactive defense across Salesforce’s global ecosystem.

As a senior technical contributor, you will directly shape the architecture, reliability, and safety of AI‑driven security systems that reason, decide, and act at machine speed.

• Design and implement scalable data models, domain contracts, and schemas with strong guarantees on performance, integrity, lineage, and governance.
• Build and optimize batch and streaming pipelines (ETL/ELT, near‑real‑time) with clear SLAs on latency, quality, and cost.
• Drive platform reliability through observability primitives including SLIs/SLOs, freshness and completeness checks, lineage tracking, and automated parity tests.

• Develop, validate, and deploy statistical and ML models for security use cases such as anomaly detection, behavioral modeling, and risk scoring.
• Productionize models as reliable services with well‑defined APIs, feature stores, versioning, and continuous monitoring for drift, bias, and performance.
• Translate large‑scale security telemetry into actionable risk intelligence and automated decisions.

• Design and deliver agentic workflows that combine perception, reasoning, and action to reduce time‑to‑detection and time‑to‑mitigation.
• Integrate LLMs with security pipelines to automate root‑cause analysis, contextual explanations, investigation summaries, and response orchestration.
• Build multi‑agent systems with role specialization, delegation, handoffs, and safe execution boundaries.
• Implement retrieval and memory at scale using RAG, hybrid search, re‑ranking, and grounding strategies with strict token and cost controls.

• Ship secure, well‑tested software that embeds ML and agentic workflows into production services, APIs, and internal platforms.
• Expose read‑only and action APIs for downstream systems and dashboards (e.g., executive, SOC, and customer‑facing views).
• Integrate with internal tooling and action systems while enforcing idempotency, retries, and side‑effect control.

• Design autonomy envelopes including manual,…