Lead Machine Learning Engineer

Salesforce is seeking a Lead Machine Learning Engineer based in San Francisco, CA (onsite) to lead a foundation machine learning platform team. The mission centers on building and accelerating scalable, resilient ML pipelines across the security engineering organization, with ownership spanning technical strategy, end-to-end pipeline development, and productionization of generative AI and agentic workflows.

What you’ll do

• Define and drive the technical ML strategy, with emphasis on robust, high-performing model architectures and disciplined MLOps practices.
• Lead end-to-end ML pipeline development, including automated retraining workflows and model optimization targeted at cost and performance.
• Own a portfolio of multiple machine learning pipelines across security and compliance initiatives.
• Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation, including model monitoring and drift detection.
• Design and implement monitoring solutions covering model performance, data quality, and overall system health.
• Partner across Data Science, Data Engineering, Research, and Product Management teams to deliver scalable ML solutions with measurable impact.
• Provide technical leadership and mentor junior machine learning engineers on ML engineering best practices.

What you bring

• Masters or PhD in a quantitative field.
• 6+ years experience across the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment.
• Deep, practical experience with containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Strong Python skills, including leading ML frameworks such as TensorFlow and PyTorch, with a focus on software engineering best practices.
• Proven ability to implement comprehensive MLOps, including CI/CD, testing practices, and model performance monitoring.
• Solid foundation in feature engineering, including implementing feature stores.
• Significant experience developing and deploying generative AI solutions into production environments.
• Expertise in infrastructure-as-code principles, monitoring tools, and big data technologies such as Spark and Snowflake.
• Experience formulating ML governance policies and ensuring adherence to data security regulations.
• Track record of leading ML initiatives that deliver significant and quantifiable business outcomes.
• Exceptional collaboration skills across Data Science, Platform Engineering, Research, and Product teams.

Technologies

• Python, TensorFlow, PyTorch
• Docker, Kubernetes, Apache Airflow
• CI/CD pipelines
• Spark, Snowflake
• Generative AI, agentic workflows
• Feature stores
• Infrastructure-as-code
• Monitoring tools
• ML governance policies

Compensation

Estimated annual salary range: USD 172,500 - 285,800.

Benefits

• Time off programs
• Medical, dental, vision
• Mental health support
• Paid parental leave
• Life and disability insurance
• 401(k)
• Employee stock purchasing program

Preferred skills

• Expertise in advanced Natural Language Processing (NLP) methodologies.
• Experience conducting research or collaborating with ML research teams.
• Experience mentoring junior engineers.
• Track record of publications and/or patents in quantitative disciplines.