Description
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.
Role Overview
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.
Key Responsibilities
Security Data Platforms & Architecture
* 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.
Machine Learning, Analytics & Risk Decisioning
* 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.
Agentic AI & LLM-Powered Security (Core Focus)
* 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.
Production Systems, APIs & Integration
* 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.
Safety, Reliability & Governance
* Design autonomy envelopes including manual, confirm, and fully automated modes with policy enforcement, approvals, spend caps, and blast-radius limits.
* Build end-to-end observability across agent lifecycles, from signal ingestion through planning, tool execution, and outcome verification.
* Implement reliability patterns such as bounded loops, circuit breakers, dead-letter queues, compensating actions, and deterministic fallbacks.
* Ensure secure-by-design handling of sensitive data, complete audit trails, RBAC/ABAC enforcement, and compliance with privacy and regulatory requirements.
Technical Leadership & Collaboration
* Provide technical leadership through architecture reviews, design discussions, and code reviews.
* Mentor engineers and data scientists, raising the quality bar across data, ML, and agentic systems.
* Partner closely with security engineers, product leaders, and infrastructure teams to translate high-impact security problems into pragmatic, scalable solutions.
* Stay current with data, ML, cloud, and agentic AI trends, introducing tools and patterns that materially improve outcomes.
Required Qualifications
* Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or equivalent practical experience.
* 8+ years of experience building and operating large-scale data or software systems with high throughput and low latency.
* Strong proficiency in Python (preferred), Scala, or Java, with excellent software engineering fundamentals.
* Expertise with data and stream processing technologies such as Airflow, Spark, Kafka, Flink, or equivalents.
* Solid SQL skills and experience with at least one NoSQL or distributed data store.
* Practical experience deploying and operating ML systems in production, including monitoring and lifecycle management.
* Cloud experience with AWS, GCP, or Azure and managed data/ML services.
* Strong understanding of statistics and machine learning methods and their real-world tradeoffs.
* Excellent communication skills, with the ability to explain complex technical concepts to diverse stakeholders.
* Working knowledge of data privacy, secure data handling, and regulatory requirements (e.g., GDPR, CCPA).
Preferred & Nice-to-Have Qualifications
* Master’s degree in Software Engineering, Data Science, or related field.
* Experience with Salesforce data and analytics platforms such as Data Cloud, Tableau/CRMA, or MuleSoft.
* MLOps and infrastructure experience with Docker, Kubernetes, Terraform, CI/CD pipelines, and canary or blue-green deployments.
* Experience with real-time analytics and streaming security use cases.
* Familiarity with agentic frameworks and patterns (planner/supervisor models, multi-agent orchestration, vector databases, model routing).
* Security domain experience with threat detection, vulnerability intelligence, asset graphs, OCSF, or runtime exploitability.
* Salesforce platform experience (Apex, LWC, APIs) or relevant certifications.
* Open-source contributions or a strong portfolio demonstrating applied ML or data engineering excellence.
Why This Role Matters
This role sits at the frontier of security, data, and agentic AI. You will help define how Salesforce moves from human-driven security operations to machine-speed, autonomous defense systems that operate safely, transparently, and at global scale.
If you are excited about building production-grade ML and agentic systems that protect real users, real data, and real infrastructure, this role offers both technical depth and industry-level impact.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.