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

Salesforce • Palo Alto, CA

5d

About The Position

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. 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.

Requirements

• 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).

Nice To Haves

• 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.

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.

Benefits

• Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program.
• More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level