Lead Machine Learning Engineer

Salesforce•San Francisco, CA

About The Position

Salesforce is seeking a hands-on lead machine learning engineer for its foundation machine learning platform team within the Trust Intelligence Platform organization. The team builds and accelerates scalable, resilient machine learning pipelines across the security engineering organization. The role defines and executes technical ML strategy and delivers measurable business impact within security and compliance requirements. The position covers the full lifecycle of machine learning pipelines, including MLOps, infrastructure-as-code, CI/CD, monitoring, automated retraining, and model optimization. The lead owns multiple ML pipelines, collaborates with cross-functional teams, and provides technical leadership and mentorship. A key focus is productizing generative AI solutions and agentic workflows to improve security and resilience for Salesforce and its customers.

Requirements

Master's or PhD in a quantitative field.
At least 6 years of experience across the end-to-end lifecycle of multi-model machine learning systems, from design and development through large-scale deployment.
Deep understanding and practical application of Docker and workflow orchestration tools such as Kubernetes and Apache Airflow for automated ML pipelines.
Mastery of Python, including leading ML frameworks such as TensorFlow and PyTorch, and adherence to software engineering best practices.
Demonstrated success implementing MLOps methodologies, including CI/CD pipelines, testing protocols, and model performance monitoring.
Solid foundation in feature engineering and implementation of feature stores.
Significant experience developing and deploying generative AI solutions in 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 machine learning initiatives that deliver significant, quantifiable business outcomes.
Exceptional collaboration skills and ability to work across Data Science, Platform Engineering, Research, and Product teams.

Nice To Haves

Expertise in advanced Natural Language Processing (NLP) methodologies.
Experience conducting research or collaborating with Machine Learning research teams.
Previous experience mentoring junior engineers.
Publications and/or patents in quantitative disciplines.

Responsibilities

Define and drive technical ML strategy, emphasizing robust, performant model architectures and MLOps practices.
Lead end-to-end development of ML pipelines, including automated retraining workflows and model optimization for cost and performance.
Own a portfolio of multiple machine learning pipelines within security and compliance.
Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation, including model monitoring and drift detection.
Design and implement monitoring for model performance, data quality, and system health.
Collaborate with Data Science, Data Engineering, Research, and Product Management teams to deliver scalable ML solutions with measurable impact.
Provide technical leadership in ML engineering best practices and mentor junior machine learning engineers.
Productize generative AI solutions and agentic workflows to enhance security posture and organizational resilience.