Lead Machine Learning Engineer

Salesforce is seeking a Lead Machine Learning Engineer to help build and accelerate a foundation machine learning platform team focused on scalable and resilient ML pipelines across the security engineering organization. In this onsite role in San Francisco, you will define and execute the technical ML strategy while owning end-to-end pipeline development, MLOps, monitoring, and infrastructure-as-code, including productizing Generative AI and agentic workflows to strengthen security posture and resilience.

The position spans technical leadership and delivery: from robust model architectures and automated retraining to CI/CD, drift detection, and comprehensive monitoring across model performance, data quality, and system health. You will also partner closely with Data Science, Data Engineering, Research, and Product Management to deliver measurable impact across security and compliance.

Responsibilities

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

Requirements

• Masters or PhD in a quantitative field.
• 6+ years of experience across the end-to-end lifecycle of multi-model machine learning systems, from design and development to large-scale deployment.
• Strong practical knowledge of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
• Proficiency in Python, including experience leading ML development with TensorFlow and PyTorch, with adherence to software engineering best practices.
• Proven success implementing MLOps methods, including CI/CD pipelines, testing, and model performance monitoring.
• Solid foundation in feature engineering, including implementation of feature stores.
• Significant experience developing and deploying generative AI solutions into production environments.
• Expertise in infrastructure-as-code, monitoring tools, and big data technologies such as Spark and Snowflake.
• Experience formulating ML governance policies and ensuring compliance with data security regulations.
• Track record of leading ML initiatives and delivering significant, 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, CI/CD pipelines
• Spark, Snowflake
• Generative AI, agentic workflows
• Feature stores
• Infrastructure-as-code, monitoring tools
• ML governance policies

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.
• Demonstrated experience conducting research or collaborating with ML research teams.
• Prior experience mentoring junior engineers.
• Track record of publications and/or patents in quantitative disciplines.

Location: San Francisco, CA (onsite)
Salary: USD 172,500 - 285,800 per yearly
Minimum Experience: 6 years