Salesforce Staff ML Engineer 

United States, California, San Francisco 

45787873

Job Category

Software Engineering

Job Details

Responsibilities

• Define and drive the technical ML strategy with emphasis on robust, performant model architectures and MLOps practices
• Lead end-to-end ML pipeline development focusing on automated retraining workflows and model optimization for cost and performance
• Implementinfrastructure-as-code,CI/CD pipelines, and MLOps automation with focus on model monitoring and drift detection
• Own the MLOps lifecycle including model governance, testing standards, and incident response for production ML systems
• Establish and enforce engineering standards for model deployment, testing, version control, and code quality
• Design and implement comprehensive monitoring solutions for model performance, data quality, and system health
• Collaborate with Data Science, Data Engineering, and Product Management teams to deliver scalable ML solutions with measurable impact
• Provide technical leadership in ML engineering best practices and mentor junior engineers in MLOps principles

Position Requirements

• MS or PhD in Computer Science, AI/ML, Software Engineering, or related field
• 8+ years of experience building and deploying ML model pipelines at scale, with focus on marketing use cases
• Expert-level knowledge of AWS services, particularly SageMaker and MLflow, for comprehensive ML experiment tracking and model lifecycle management
• Deep expertise in containerization and workflow orchestration (Docker, Kubernetes, Apache Airflow) for ML pipeline automation
• Advanced Python programming with expertise in ML frameworks (TensorFlow, PyTorch) and software engineering best practices
• Proven experience implementing end-to-end MLOps practices including CI/CD, testing frameworks, and model monitoring
• Strong background in feature engineering and feature store implementations using cloud-native technologies
• Expert ininfrastructure-as-code,monitoring solutions, and big data technologies (Spark, Snowflake)
• Experience defining ML governance policies and ensuring compliance with data security requirements
• Track record of leading ML initiatives that deliver measurable marketing impact
• Strong collaboration skills and ability to work effectively with Data Science and Platform Engineering teams

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Posting Statement

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