Job overview in this role, you'll have the opportunity to make an outsized impact on salesforce's marketing initiatives, helping to promote our vast product portfolio to a global customer base, including 90% of the fortune 500. By driving state-of-the-art ml solutions for our internal marketing platforms, you'll directly contribute to enhancing the effectiveness of salesforce's marketing efforts. Your ml expertise will play a pivotal role in accelerating salesforce's growth. This is a unique chance to apply your passion for ml to drive transformative business impact on a global scale, shaping the future of how salesforce engages with potential and existing customers, and contributing to our continued innovation and industry leadership in the crm and agentic enterprise space. Responsibilities define and drive the technical ml strategy with emphasis on robust, performant model architectures and mlops practices own the ml lifecycle including model governance, testing standards, and incident response for production ml systems establish and enforce engineering standards for model deployment, testing, version control, and code quality implement infrastructure-as-code, ci / cd pipelines, and ml automation with focus on model monitoring and drift detection design and implement comprehensive monitoring solutions for model performance, data quality, and system health lead end-to-end ml pipeline development focusing on optimizing model cost and performance as well as automating training workflows collaborate with data science, data engineering, and product management teams to deliver scalable ml solutions with measurable impact provide technical leadership in ml engineering best practices and mentor junior engineers in ml and mlops principles position requirements ms or phd in computer science, ai / ml, software engineering, or related field 8 years of experience building and deploying ml model pipelines at scale, with focus on marketing use cases expert-level knowledge of aws services, particularly sagemaker and related services deep expertise in containerization and workflow orchestration (eg, docker, apache airflow) for ml pipeline automation advanced python programming with expertise in ml frameworks (tensorflow, pytorch) and software engineering best practices proven experience implementing end-to-end mlops practices including ci / cd, testing frameworks, and model monitoring expert in infrastructure-as-code, monitoring solutions, and big data technologies (eg, snowflake, spark) experience implementing ml governance policies and ensuring compliance with data security requirements familiarity with feature engineering and feature store implementations using cloud-native technologies track record of leading ml initiatives that deliver measurable marketing impact strong collaboration skills and ability to work effectively with data science and platform engineering teams accommodations if you require assistance due to a disability applying for open positions please submit a request via this. Posting statement any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education. J-18808-ljbffr