Lead Machine Learning Engineer, Salesforce

• 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
• Extensive 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
• Hands-on comfort with high-volume logs and proficiency with Spark/Pyspark, Snowflake, Flink and streaming services such as Apache Kafka
• Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines
• Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices
• Demonstrated success in implementing comprehensive MLOps methodologies, encompassing CI/CD pipelines, testing protocols, and model performance monitoring
• Solid foundation in feature engineering techniques and the implementation of feature stores
• Experience in formulating ML governance policies and ensuring adherence to data security regulations
• Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership
• Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations
• 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

Desirable

• 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
• The lead will not simply build models; they will architect the data-driven strategy for our threat detection capabilities
• Shape the Defense Strategy: You will own the decision-making process—translating vague security threats into concrete mathematical problems. By championing a rapid prototyping culture, you will validate hypotheses in days rather than months, ensuring our engineering resources are focused only on high-value detections while killing low-signal ideas early
• Detect the "Unknown Unknowns": You will lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learing. Your work will 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: You will act as a force multiplier, mentoring junior scientists and engineers, and building the internal tooling, feature stores, and libraries that make the whole team faster. You will influence the broader security engineering roadmap to ensure a closed loop security telemetry that is treated as a first-class citizen
• Operationalize Intelligence: By prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience, you will deliver production-grade models that the SOC actually trusts—minimizing "alert fatigue" and maximizing analyst efficiency