Senior ML Engineer

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

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

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: