Beskrivning
Join the Marketing AI/ML Algorithms and Applications team within Salesforce's Marketing organization. In this role, you'll have the opportunity to make an outsized impact on Salesforce's marketing initiatives, helping to promote our vast product portfolio to a global customer base, including 90% of the Fortune 500. By driving state-of-the-art ML solutions for our internal marketing platforms, you'll directly contribute to enhancing the effectiveness of Salesforce's marketing efforts. Your ML expertise will play a pivotal role in accelerating Salesforce's growth. This is a unique chance to apply your passion for ML to drive transformative business impact on a global scale, shaping the future of how Salesforce engages with potential and existing customers, and contributing to our continued innovation and industry leadership in the CRM and Agentic enterprise space.
We are seeking an experienced Lead / Staff Machine Learning Engineer to support the development and deployment of high-impact ML model pipelines that measurably improve marketing performance and deliver customer value. In this critical role, you will collaborate closely with Data Science, Data Engineering, Product, and Marketing teams to lead the design, implementation, and operations of end-to-end ML solutions at scale. As a hands-on technical leader, you will own the MLOps lifecycle, establish best practices, and mentor junior engineers to help grow a world-class team that stays at the forefront of ML innovation. This is a unique opportunity to apply your passion for ML and to drive transformative business impact for the world's #1 CRM provider, shaping the future of customer engagement through AgentForce - our groundbreaking AI agents that are setting new global standards for intelligent automation.
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
- Implement infrastructure-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 in infrastructure-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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