Senior ML Engineer: Real-Time Security & MLOps

Salesforce; Washington; On-site; USD 180,000 - 260,000; Full time

Job summary

Salesforce is seeking a Senior Member of Technical Staff in Software Engineering to lead production-grade ML services for security. You will architect scalable pipelines, build low-latency inference systems, and advance MLOps practices within the Trust Intelligence Platform.

The role emphasizes real-time data streams, anomaly detection, and resilient ML deployments in a cybersecurity context. The ideal candidate has 3–5+ years in ML engineering or data science, with hands-on experience deploying

Qualifications

• Experience with streaming services and distributed processing frameworks.
• Deployment of ML models in production with monitoring and CI/CD.
• Strong Python programming and API development for real-time inference.
• Experience deploying anomaly detection and clustering in production cybersecurity.

Responsibilities

• Engineer Production-Grade Services: build low-latency real-time inference services for security data streams.
• Operationalize intelligence with automated CI/CD and model monitoring.
• Architect scalable pipelines with internal tooling, feature stores, and libraries for rapid ML scaling.
• Drive adversarial resilience with defenses against model evasion and high availability.

Skills

Streaming & high-volume data

MLOps mastery

Infrastructure & orchestration

Python Software Engineering

Domain expertise (cybersecurity)

Feature engineering

ML engineering

Technical leadership

Performance optimization

Quantitative background

Tools

Docker

Kubernetes

Apache Airflow

Apache Kafka

Flink

Ray

Spark / PySpark

Job description

Salesforce is seeking a Senior Member of Technical Staff in Software Engineering to lead production-grade ML services for security. You will architect scalable pipelines, build low-latency inference systems, and advance MLOps practices within the Trust Intelligence Platform.

The role emphasizes real-time data streams, anomaly detection, and resilient ML deployments in a cybersecurity context. The ideal candidate has 3–5+ years in ML engineering or data science, with hands-on experience deploying