Lead Machine Learning Engineer - Cyber Security (LMTS)

Overview

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.

Responsibilities

• Shape the Defense Strategy: Own the decision‑making process – translating vague security threats into concrete mathematical problems. Champion rapid prototyping culture to validate hypotheses in days rather than months, ensuring engineering resources focus on high‑value detections while eliminating low‑signal ideas early.
• Detect the "Unknown Unknowns": Lead the evolution of 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, building internal tooling, feature stores, and libraries that accelerate the team. Influence the broader security engineering roadmap to ensure closed‑loop security telemetry 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 trusts—minimizing alert fatigue and maximizing analyst efficiency.

Required skills

• 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.

Preferred skills

• Masters or PhD in a quantitative field.
• Expertise in advanced Natural Language Processing (NLP) methodologies.
• Experience contributing to open‑source security data science tools.
• Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences.
• Background in offensive security (Penetration Testing/Red Teaming) with an “attacker’s mindset.”
• Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
• Previous experience in a mentoring role for junior engineers.
• Track record of publications and/or patents in quantitative disciplines.

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.

EEO Statement

Salesforce is an equal opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment. All applicants and employees are assessed on the basis of merit, competence and qualifications without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit.