Lead Machine Learning Engineer - LMTS

About the Company

Salesforce is the #1 AI CRM. We help companies across every industry blaze new trails and connect with customers in a whole new way. Our core values guide us as we drive innovation in business with AI, data, and CRM.

Lead Member of Technical Staff – Machine Learning Engineering (Software Engineering)

• Shape the Defense Strategy by owning the decision‑making process—translating vague security threats into concrete mathematical problems.
• Detect the "Unknown Unknowns" by leading the evolution of threat detection, introducing advanced probabilistic modeling, graph analytics, supervised and unsupervised learning to expose sophisticated threats such as active system intrusions, lateral movement, beaconing, and insider attacks.
• Elevate the Organization by mentoring junior scientists and engineers, building internal tooling, feature stores, and libraries to accelerate team productivity, and influencing the broader security engineering roadmap.
• Operationalize Intelligence by prioritizing engineering rigor (CI/CD, scalable code) and adversarial resilience to deliver production‑grade models the SOC trusts—minimizing alert fatigue and maximizing analyst efficiency.

• 3–5+ years of data science experience, with at least 2+ years in cybersecurity domain designing, implementing, and deploying anomaly detection, clustering, and graph models in production.
• Hands‑on experience with high‑volume logs and proficiency with Spark/PySpark, Snowflake, Flink, and streaming services such as Apache Kafka.
• Deep understanding of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Mastery of Python programming and leading ML frameworks (Tensor Flow, PyTorch); 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 implementation of feature stores.
• Experience 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 structure data‑driven solutions to vague business problems without a predefined roadmap.

• Master’s or Ph.D. in a quantitative field.
• Expertise in advanced NLP methodologies.
• Experience contributing to open‑source security data science tools.
• Presentations at major security conferences (Black Hat, DEFCON, 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 ML research teams.
• Previous experience in a mentoring role for junior engineers.
• Track record of publications and/or patents in quantitative disciplines.

Time‑off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), employee stock‑purchasing program, and more.

If you need a reasonable accommodation during the application or recruiting process, please submit a request via the Accommodations Request Form.

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Certain roles may be eligible for incentive compensation, equity, and benefits.