Lead ML Engineer

You will be joining Salesforce, the Customer Company, known for inspiring the future of business by combining AI, data, and CRM technologies. As part of the Marketing AI/ML Algorithms and Applications team, you will play a crucial role in enhancing Salesforce's marketing initiatives by implementing cutting-edge machine learning solutions. Your work will directly impact the effectiveness of marketing efforts, contributing to Salesforce's growth and innovation in the CRM and Agentic enterprise space. In the position of Lead / Staff Machine Learning Engineer, you will be responsible for developing and deploying ML model pipelines that drive marketing performance and deliver customer value. Working closely with cross-functional teams, you will lead the design, implementation, and operations of end-to-end ML solutions at scale. Your role will involve establishing best practices, mentoring junior engineers, and ensuring the team remains at the forefront of ML innovation. Key Responsibilities: - Define and drive the technical ML strategy, emphasizing robust model architectures and MLOps practices - Lead end-to-end ML pipeline development, focusing on automated retraining workflows and model optimization - Implement infrastructure-as-code, CI/CD pipelines, and MLOps automation for model monitoring and drift detection - Own the MLOps lifecycle, including model governance, testing standards, and incident response for production ML systems - Establish engineering standards for model deployment, testing, version control, and code quality - Design and implement monitoring solutions for model performance, data quality, and system health - Collaborate with cross-functional teams to deliver scalable ML solutions with measurable impact - Provide technical leadership in ML engineering best practices and mentor junior engineers in MLOps principles Position Requirements: - 8+ years of experience in building and deploying ML model pipelines with a focus on marketing - Expertise in AWS services, particularly SageMaker and MLflow, for ML experiment tracking and lifecycle management - Proficiency in containerization, workflow orchestration, Python programming, ML frameworks, and software engineering best practices - Experience with MLOps practices, feature engineering, feature store implementations, and big data technologies - Track record of leading ML initiatives with measurable marketing impact and strong collaboration skills Join us at Salesforce to drive transformative business impact and shape the future of customer engagement through innovative AI solutions.,