Staff ML Engineer
Description For Staff ML Engineer
Join Salesforce's Marketing AI/ML Algorithms and Applications team as a Staff ML Engineer and make a significant impact on Salesforce's marketing initiatives. This role offers an opportunity to drive state-of-the-art ML solutions for internal marketing platforms, directly contributing to the effectiveness of Salesforce's global marketing efforts reaching 90% of the Fortune 500.
As a Staff Machine Learning Engineer, you'll lead the development and deployment of high-impact ML model pipelines that measurably improve marketing performance. You'll collaborate with Data Science, Data Engineering, Product, and Marketing teams to design and implement end-to-end ML solutions at scale. Your responsibilities include owning the MLOps lifecycle, establishing best practices, and mentoring junior engineers.
The position requires expertise in AWS services, particularly SageMaker and MLflow, along with deep knowledge of containerization and workflow orchestration using Docker, Kubernetes, and Apache Airflow. You'll work with cutting-edge technologies including TensorFlow, PyTorch, and various big data technologies while ensuring compliance with data security requirements.
This role presents a unique opportunity to shape the future of customer engagement through AgentForce, Salesforce's groundbreaking AI agents. You'll be at the forefront of ML innovation, driving transformative business impact for the world's leading CRM provider. The position offers competitive compensation and the chance to work with a world-class team in either San Francisco or Chicago.
Responsibilities For Staff ML Engineer
- Define and drive technical ML strategy with emphasis on robust model architectures and MLOps practices
- Lead end-to-end ML pipeline development focusing on automated retraining workflows
- Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation
- Own the MLOps lifecycle including model governance and testing standards
- Establish and enforce engineering standards for model deployment
- Design and implement comprehensive monitoring solutions
- Collaborate with Data Science, Data Engineering, and Product Management teams
- Provide technical leadership and mentor junior engineers
Requirements For Staff ML Engineer
- 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
- Expert-level knowledge of AWS services, particularly SageMaker and MLflow
- Deep expertise in containerization and workflow orchestration
- Advanced Python programming with expertise in ML frameworks
- Experience in feature engineering and feature store implementations
- Expert in infrastructure-as-code and big data technologies
- Strong collaboration skills
- Track record of leading ML initiatives
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