Staff ML Engineer
| Company | Salesforce |
|---|---|
| Location | San Francisco, CA, USA, Chicago, IL, USA |
| Salary | $184000 – $276100 |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
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
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
Preferred Qualifications
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No preferred qualifications provided.