Salesforce develops cloud-based software centered on a customer relationship management platform that brings company data and teams together through an integrated, AI-driven system. Its products support sales, customer service, and marketing workflows, with solutions designed for businesses ranging from small and medium-sized enterprises to larger organizations. Salesforce also provides industry-focused tools, Trailhead learning resources, and AppExchange apps that extend the capabilities of its CRM platform.
Lead Machine Learning Engineer
Set technical direction for secure, scalable machine learning pipelines at Salesforce, including automated retraining and MLOps. Bring generative AI solutions and agent workflows into production to strengthen security and compliance.
United States
California
Washington$172,500 – $260,100 / year
On-siteFull-timeSeniorEnglish RequiredMachine Learning EngineerDescription
- Set technical direction for machine learning strategy.
- Lead development of machine learning pipelines, including automated retraining and optimization for cost and performance.
- Manage a portfolio of machine learning pipelines for security and compliance.
- Build infrastructure as code, CI/CD pipelines, and MLOps automation.
- Track model performance, data quality, system health, and model drift.
- Work with Data Science, Data Engineering, Research, and Product Management teams.
- Guide engineering practices for machine learning.
- Mentor junior machine learning engineers.
- Bring generative AI solutions and agent workflows into production to improve organizational and customer security and resilience.
Requirements
- Hold a master’s or PhD in a quantitative field.
- Have 6+ years of experience across the lifecycle of multi-model machine learning systems, from design and development through large-scale deployment.
- Have a strong grasp of Docker and practical experience with Kubernetes and Apache Airflow for workflow orchestration.
- Be highly proficient in Python.
- Be proficient with TensorFlow and PyTorch.
- Apply software engineering best practices.
- Have experience with MLOps, including CI/CD pipelines, testing, and model performance monitoring.
- Have experience with feature engineering and feature stores.
- Have substantial experience developing and deploying generative AI solutions in production.
- Bring expertise in infrastructure as code, monitoring tools, Spark, and Snowflake.
- Have experience developing ML governance policies and ensuring compliance with data security regulations.
- Have led machine learning initiatives with significant, measurable business outcomes.
- Work effectively with Data Science, Platform Engineering, Research, and Product teams.
- Advanced Natural Language Processing (NLP) expertise is preferred.
- Research experience or collaboration with machine learning research teams is preferred.
- Previous mentoring experience is preferred.
- Publications or patents in quantitative disciplines are preferred.
Benefits
- Time off programs
- Medical insurance
- Dental insurance
- Vision insurance
- Mental health support
- Paid parental leave
- Life insurance
- Disability insurance
- 401(k)
- Employee stock purchasing program
- Incentive compensation may be available for certain roles
- Equity may be available for certain roles