McAfee

Senior AI Engineer, MarTech

McAfee
US San Jose, CA, US
Onsite 2026-07-02
Announced salary
$135,910 - $223,285
Low
$116K
Median
$153K
High
$202K
Market in San Jose · BLS OEWS 2025
Estimated net pay
$7,988 - $12,434
/month · 29% withheld
after tax & contributions · Single, no dependents

Job description

Role Overview: We are looking for a Senior AI Engineer to lead the transformation of our marketing ecosystem. You won’t just be maintaining tools; you will be architecting the intelligence layer that powers hyper\-personalization, autonomous campaign optimization, and generative creative pipelines. You will architect and lead buildout of infrastructure systems that do not merely execute pre\-defined tasks but perceive context, reason through complex strategic problems, and orchestrate end\-to\-end workflows with minimal human intervention. We are moving from "campaign management" —a manual, administrative task—to "campaign orchestration" a strategic, supervisory role and invest in effective Human\-Agent Teams. This is a position located in the US in either San Jose, CA or Frisco, TX. You will be required to be onsite on an as\-needed basis. We are only considering candidates within a commutable distance to one of the two locations and are not offering relocation assistance at this time. Position Details: ***About the Role:*** * **Architect Agentic Workflows:** Design and deploy AI agents to automate complex marketing tasks such as cross\-channel campaign orchestration and real\-time lead qualification. * **Generative Asset Pipelines:** Build and maintain scalable pipelines for automated ad creative generation (text, image, and video) using LLMs and Multimodal models (Stable Diffusion, GPT\-4o, Sora) while ensuring brand\-safe guardrails. * **Real\-time Personalization:** Implement RAG (Retrieval\-Augmented Generation) systems to provide context\-aware, personalized content across web, email, and SMS. * **Build Predictive Models:** Develop and productionalize ML models for high\-impact marketing use cases: LTV (Lifetime Value) prediction, churn propensity, and "Next Best Action" engines. * **MLOps \& Integration:** Own the end\-to\-end lifecycle of models, from feature engineering in SQL/Python to deployment via APIs and monitoring for data drift in production. * **

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