Lead Machine Learning Engineer - LMTS

Company Overview

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.

Senior / Lead Member of Technical Staff – Machine Learning Engineering

We are a foundation machine learning platform team within the Trust Intelligence Platform organization with a main focus to build and accelerate scalable and resilient machine learning pipelines across the security engineering organization.

We are looking for a highly motivated, hands‑on lead machine learning engineer with a strong business understanding of cybersecurity problems, who acts as a force multiplier security data scientist for our security organization. The lead will not simply build models; they will architect the data‑driven strategy for our threat detection capabilities.

• Shape the Defense Strategy: Own the decision‑making process—translating vague security threats into concrete mathematical problems. Champion a rapid prototyping culture to validate hypotheses in days rather than months, ensuring engineering resources focus only on high‑value detections while eliminating low‑signal ideas early.
• Detect the “Unknown Unknowns”: Lead the evolution of our threat detection, introducing advanced probabilistic modeling, graph analytics, supervised and unsupervised learning. Expose sophisticated threats—such as active system intrusions, lateral movement, beaconing, and insider attacks—that evade traditional defenses, directly reducing the organization’s risk surface.
• Elevate the Organization: Act as a force multiplier, mentoring junior scientists and engineers, and building internal tooling, feature stores, and libraries that make the whole team faster. Influence the broader security engineering roadmap to ensure a closed‑loop security telemetry that is treated as a first‑class citizen.
• Operationalize Intelligence: Prioritize engineering rigor (CI/CD, scalable code) and adversarial resilience to deliver production‑grade models that the SOC actually trusts—minimizing alert fatigue and maximizing analyst efficiency.

• Data Science
  
  Experience:
  
  3–5+ years in data science, with at least 2+ years dedicated to the cybersecurity domain designing, implementing, and deploying systems of anomaly detection, clustering, and graph models in production.
• Large‑Scale Data & Technology: Hands‑on comfort with high‑volume logs and proficiency with Spark/PySpark, Snowflake, Flink and streaming services such as Apache Kafka.
• Containerization & Orchestration: Deep understanding and application of Docker and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Python & ML Frameworks: Mastery of Python programming, including proficiency in leading ML frameworks (Tensor Flow, PyTorch) and adherence to software engineering best practices.
• MLOps Expertise: Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring.
• Feature Engineering: Solid foundation in feature engineering techniques and the implementation of feature stores.
• Governance & Security: Experience in formulating ML governance policies and ensuring adherence to data security regulations.
• Stakeholder Communication: Ability to explain complex statistical concepts to non‑technical stakeholders and executive leadership.
• Project Management: Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations.
• Autonomy: High degree of autonomy with the ability to look at a vague business problem and structure a data‑driven solution without needing a predefined roadmap.

• Advanced
  
  Education:
  
  Masters or PhD in a quantitative field.
• NLP Expertise: Expertise in advanced Natural Language Processing methodologies.
• Open‑Source Contribution: Experience contributing to open‑source…