Senior Member of Technical Staff - Senior Machine Learning Engineering

Job Category: Software Engineering

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

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 senior 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 candidate will not simply build models; they will architect the data-driven strategy for our threat detection capabilities.

Your impact:

• Engineer Production-Grade Services: You will be responsible for building and maintaining low-latency, real-time inference services designed to handle heavy security data streams. You will ensure that sophisticated models—including graph analytics and supervised learning—are deployed into production environments with the performance required to intercept active threats in real-time.

• Operationalize Intelligence: You will prioritize engineering rigor by implementing advanced MLOps methodologies, including automated CI/CD pipelines, robust testing protocols, and comprehensive model performance monitoring. Your goal is to deliver models that the SOC trusts implicitly by minimizing alert fatigue through high-fidelity, production-hardened detections.

• Architect Scalable Pipelines: You will influence the security engineering roadmap by building the internal tooling, feature stores, and libraries that enable rapid scaling of machine learning services. You will treat security telemetry as a first-class citizen, ensuring a closed-loop system for automated response and mitigation.

• Drive Adversarial Resilience: You will ensure that all production services are built with an "attacker's mindset," implementing defenses against model evasion and ensuring high availability under high-volume load.

Required skills:

• Streaming & High-Volume Data: Extensive hands-on experience with streaming services and distributed processing frameworks, specifically Apache Kafka, Flink, Ray and Spark/Pyspark.

• MLOps Mastery: Demonstrated success in implementing comprehensive MLOps methodologies for high-volume data, encompassing automated deployment, CI/CD, and real-time performance monitoring.

• Infrastructure & Orchestration: Deep understanding of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for managing automated, production-grade ML pipelines.

• Software Engineering Excellence: Mastery of Python programming with an emphasis on software engineering best practices, including scalable code design and API development for real-time inference.

• Domain Expertise: 3-5+ years in machine learning engineering or data science, with at least 2+ years dedicated to deploying anomaly detection and clustering systems in a production cybersecurity environment.

• Feature Engineering: Solid foundation in implementing feature stores and low-latency feature retrieval techniques for real-time model scoring.

• ML Engineering: Demonstrated experience building and deploying ML models to production, conducting research or working collaboratively with Machine Learning (ML) research teams.

• Technical Leadership: Ability to take ownership of complex engineering problems, structure data-driven solutions, and work with minimal supervision.

Preferred skills:

• Performance Optimization: Experience tuning high-throughput systems for low-latency requirements.

• Quantitative Background: Masters or PhD in a quantitative field.

• Adversarial Security: Background in offensive security or Red Teaming to improve system resilience.

• Security Frameworks: Practical knowledge of security frameworks such as MITRE ATT&CK and OCSF to inform the engineering of detection logic.

• Mentorship: Previous experience in a mentoring role for junior engineers, specifically in production engineering and MLOps.