Salesforce

1 month ago

Lead Machine Learning Engineer - LMTS

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Lead member of a foundation ML platform team building scalable, production-grade ML pipelines for security analytics. Architect data-driven threat detection strategies, mentor junior engineers, and deliver robust models with CI/CD, feature stores, and governance. Lead efforts across high-volume logs, Spark/PySpark, Snowflake, Kafka, and containerized workflows to reduce risk and improve SOC efficiency.

Location: San Francisco, Palo Alto, Bellevue

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Shape the Defense Strategy: own the decision-making process—translate vague security threats into concrete mathematical problems and validate hypotheses quickly to focus on high-value detections.
•Detect the Unknown Unknowns: lead evolution of threat detection with probabilistic modeling, graph analytics, and supervised/unsupervised learning to expose sophisticated threats.
•Elevate the Organization: mentor junior scientists and engineers, build internal tooling and feature stores, and influence the security engineering roadmap.
•Operationalize Intelligence: deliver production-grade models with CI/CD and adversarial resilience to minimize alert fatigue and improve analyst efficiency.
•Collaborate Across Stakeholders: communicate complex statistical concepts to non-technical leadership and manage scope/timelines across multiple teams.

Pay and Benefits

Salary: USD 172,500 - 260,100 annually

Perks:Health Insurance401kEquityMedicalDental

Key Requirements

•Extensive data science experience (3-5+ years) with at least 2+ years in cybersecurity designing, implementing and deploying anomaly detection, clustering, and graph models in production.
•Hands-on with high-volume logs and proficiency in 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 with ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
•Proven success implementing end-to-end MLOps, including CI/CD pipelines, testing protocols, and model performance monitoring.

Experience:3-5 years

Skills:CommunicationProblem-solvingMentoring

Languages:English

Tech Stack:SparkPySparkSnowflakeFlinkApache KafkaDockerKubernetesApache AirflowPythonTensorFlowPyTorch

Company Brief

Salesforce

Provides a leading cloud-based customer relationship management (CRM) platform with sales, service, marketing, analytics, and integration tools that empower businesses to manage customer relationships and digital transformation at scale.

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor

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Salesforce

1 month ago

Lead Machine Learning Engineer - LMTS

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Location: San Francisco, Palo Alto, Bellevue

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Sr. Manager level

Key Responsibilities

•Shape the Defense Strategy: own the decision-making process—translate vague security threats into concrete mathematical problems and validate hypotheses quickly to focus on high-value detections.
•Detect the Unknown Unknowns: lead evolution of threat detection with probabilistic modeling, graph analytics, and supervised/unsupervised learning to expose sophisticated threats.
•Elevate the Organization: mentor junior scientists and engineers, build internal tooling and feature stores, and influence the security engineering roadmap.
•Operationalize Intelligence: deliver production-grade models with CI/CD and adversarial resilience to minimize alert fatigue and improve analyst efficiency.
•Collaborate Across Stakeholders: communicate complex statistical concepts to non-technical leadership and manage scope/timelines across multiple teams.

Pay and Benefits

Salary: USD 172,500 - 260,100 annually

Perks:Health Insurance401kEquityMedicalDental

Key Requirements

•Extensive data science experience (3-5+ years) with at least 2+ years in cybersecurity designing, implementing and deploying anomaly detection, clustering, and graph models in production.
•Hands-on with high-volume logs and proficiency in 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 with ML frameworks (TensorFlow, PyTorch) and adherence to software engineering best practices.
•Proven success implementing end-to-end MLOps, including CI/CD pipelines, testing protocols, and model performance monitoring.

Experience:3-5 years

Skills:CommunicationProblem-solvingMentoring

Languages:English

Tech Stack:SparkPySparkSnowflakeFlinkApache KafkaDockerKubernetesApache AirflowPythonTensorFlowPyTorch

Company Brief

Salesforce

Industry: SaaS

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 1999

Glassdoor: 4.1

Website LinkedIn Glassdoor