Lead Machine Learning Engineer - LMTS

Description

Lead member of technical staff - Machine Learning Engineering

Job Category:
Software Engineering

Job Details

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: 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 learning. 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.

• 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 (Tensor Flow, 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.

• 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.

For roles in San Francisco and Los Angeles:
Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.