Lead Machine Learning Engineer

Salesforce

Lead Machine Learning Engineer at Salesforce developing advanced AI solutions for cybersecurity threats. Responsible for mentoring engineers and enhancing threat detection capabilities with innovative methodologies.

Posted 6/19/2026full-timeSan Francisco • California, Washington • 🇺🇸 United StatesSenior💰 $172,500 - $260,100 per yearWebsite

Tech Stack

Tools & technologies

AirflowApacheCyber SecurityDockerKafkaKubernetesPySparkPythonPyTorchSparkTensorflow

About the role

Key responsibilities & impact

Shape the Defense Strategy: Own the decision-making process—translating vague security threats into concrete mathematical problems
Detect the "Unknown Unknowns": Lead the evolution of our threat detection, introducing more advanced probabilistic modeling, graph analytics, supervised and unsupervised learning.
Elevate the Organization: Act as a force multiplier, mentoring junior scientists and engineers, building internal tooling, feature stores, and libraries.
Operationalize Intelligence: Deliver production-grade models that the SOC actually trusts—minimizing "alert fatigue" and maximizing analyst efficiency.

Requirements

What you’ll need

Extensive experience (3-5+ years) in data science, with at least 2+ years in the cybersecurity domain designing, implementing and deploying systems of anomaly detection
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.

Benefits

Comp & perks

time off programs
medical
dental
vision
mental health support
paid parental leave
life and disability insurance
401(k)
employee stock purchasing program

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data scienceanomaly detectionprobabilistic modelinggraph analyticssupervised learningunsupervised learningfeature engineeringMLOpsCI/CD pipelinesmodel performance monitoring

Soft Skills

mentoringcommunicationstakeholder managementproblem-solvingautonomy