Lead / Staff Ml Engineer

Lead / Staff ML Engineer — Mexico CityWe're Salesforce, the Customer Company, inspiring the future of business with AI + Data + CRM and pioneering the next frontier of enterprise AI with AgentForce.

In this role, you will drive state-of-the-art ML solutions for our internal marketing platforms to promote our product portfolio to a global customer base and accelerate Salesforce's growth.

You will collaborate with Data Science, Data Engineering, Product, and Marketing teams to lead the design, implementation, and operations of end-to-end ML solutions at scale, own the ML lifecycle, establish best practices, and mentor junior engineers to grow a world-class ML engineering team.ResponsibilitiesDefine and drive the technical ML strategy with emphasis on robust, performant model architectures and MLOps practicesOwn the ML lifecycle including model governance, testing standards, and incident response for production ML systemsEstablish and enforce engineering standards for model deployment, testing, version control, and code qualityImplement infrastructure-as-code, CI / CD pipelines, and ML automation with focus on model monitoring and drift detectionDesign and implement comprehensive monitoring solutions for model performance, data quality, and system healthLead end-to-end ML pipeline development focusing on optimizing model cost and performance as well as automating training workflowsCollaborate with Data Science, Data Engineering, and Product Management teams to deliver scalable ML solutions with measurable impactProvide technical leadership in ML engineering best practices and mentor junior engineers in ML and MLOps principlesPosition RequirementsMS or PhD in Computer Science, AI / ML, Software Engineering, or related field8+ years of experience building and deploying ML model pipelines at scale, with focus on marketing use casesExpert-level knowledge of AWS services, particularly SageMaker and related servicesDeep expertise in containerization and workflow orchestration (e.g., Docker, Apache Airflow) for ML pipeline automationAdvanced Python programming with expertise in ML frameworks (TensorFlow, PyTorch) and software engineering best practicesProven experience implementing end-to-end MLOps practices including CI / CD, testing frameworks, and model monitoringExpert in infrastructure-as-code, monitoring solutions, and big data technologies (e.g., Snowflake, Spark)Experience implementing ML governance policies and ensuring compliance with data security requirementsFamiliarity with feature engineering and feature store implementations using cloud-native technologiesTrack record of leading ML initiatives that deliver measurable marketing impactStrong collaboration skills and ability to work effectively with Data Science and Platform Engineering teamsSeniority levelMid-Senior levelEmployment typeFull-timeJob functionEngineering and Information TechnologyIndustriesIT Services and IT Consulting

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