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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Machine Learning Engineer - Cyber Security - **Company:** Salesforce.com, Inc. - **Location:** Palo Alto, CA, United States - **Experience:** Expert - **Salary:** $172,500.0 - $260,100.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Apache HTTP Server, Software System Penetration Testing, Collaborative Learning, Cyber Security, Continuous Delivery, Continuous Integration, Data Security, Internet Security, Python (Programming Language), Machine Learning, Natural Language Processing, Open Source Technology, Systems Development Life Cycle, Rapid Prototyping Process, Tensorflow, Salesforce.Com, Software Construction, Software Engineering, Data Streaming, Scripting, Feature Engineering, Pytorch, Snowflake, Apache Spark, Data Strategy, Containerization, Pyspark, Kubernetes, Apache Flink, Data Analytics, Apache Kafka, Machine Learning Operations, Docker - **Published:** August 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/lead-machine-learning-engineer-cyber-security-lmts-palo-alto-ca--72fbe8e7-bea4-46c3-9920-3ef56277f17c ## About the Role 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., * 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., Analysis Skills, Apache, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Best Practices, Collaborative Learning, Computer Security, Concrete, Conferences, Continuous Deployment/Delivery, Continuous Integration, Customer Relationship Management (CRM), Data Science, Docker, Establish Priorities, Information/Data Security (InfoSec), Intelligence Agencies, Internet Security, Leadership, Machine Learning, Machine Tool, Maintain Compliance, Mathematics, Mentoring, Natural Language Processing (NLP), Open Source, Penetration Testing, Performance Analysis, Performance Modeling, Policy Development, Python Programming/Scripting Language, Rapid Prototyping, Regulatory Compliance, Research Skills, Risk, Salesforce.com, Scalable System Development, Security Attacks, Software Engineering, Telemetry ## Description * 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 learing. 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Dev Digest 134 - Where pixels sing?](https://www.wearedevelopers.com/magazine/477-dev-digest-134-where-pixels-sing)