Principal AI/ML Engineer - AdTech

Zeta Global is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence to enhance marketing efficiency. The Principal AI/ML Engineer will drive the development of machine learning models and AI-driven features for the advertising platform, collaborating with various teams to ensure high-performance and scalable ML systems.

Responsibilities:

• Lead the design and implementation of scalable, high-performance, and resilient ML solutions for AdTech use cases. You will set technical direction for integrating AI/ML into our Demand-Side Platform and broader ad tech stack
• Architect and evolve the end-to-end machine learning pipeline – from data ingestion and training to real-time inference, for our real-time bidding, targeting, and optimization algorithms. Ensure that models seamlessly integrate with our ad serving architecture and handle low-latency, high-throughput requirements
• Define the technical roadmap and vision for AI/ML in our platform, evaluating new tools and techniques (including the latest in deep learning and LLMs) and making strategic build-vs-buy decisions. Continuously assess emerging technologies to keep our AdTech capabilities on the cutting edge
• Develop intelligent systems using AI agents and agentic workflows to automate and optimize end-to-end campaign processes. Leverage LLMs and generative AI to enable autonomous campaign management tasks such as audience segmentation, dynamic bid adjustments, and creative asset generation
• Partner with engineering, product, and data science teams to translate marketing objectives into ML-driven solutions. Work closely with stakeholders to deliver innovative features – including those powered by Large Language Models (LLMs), that enhance our advertising products
• Ensure system robustness and stability for ML services in a high-concurrency, low-latency environment. Optimize algorithms and infrastructure for speed and scalability, and implement monitoring to maintain model performance and uptime in production
• Provide technical guidance and mentorship to other engineers and data scientists, fostering a culture of excellence in engineering and ML best practices. Review code and models, share knowledge, and champion continuous improvement across teams

Requirements:

• 10+ years of experience in software engineering or data science, with at least 3-5 years in a principal engineer or lead ML role (preferably in the AdTech/MarTech industry)
• Proven experience designing and building high-throughput, low-latency distributed systems or data pipelines for large-scale applications
• Deep expertise in the programmatic advertising ecosystem, including Demand-Side Platforms (DSPs), real-time bidding (RTB), Supply-Side Platforms (SSPs), and ad exchanges
• Proficiency in programming languages such as Java, Go, and Python for building both data-intensive backend services and ML tools
• Hands-on experience with machine learning frameworks and libraries, especially PyTorch or TensorFlow, for developing and training models
• Strong experience with big data and streaming frameworks (e.g., Apache Spark, Kafka, Hadoop) for processing and analyzing large datasets
• Expertise with cloud platforms (preferably AWS) and related services for scalable ML model deployment and data storage
• Experience with various data stores, including both SQL and NoSQL databases (e.g., MySQL/PostgreSQL, Cassandra, DynamoDB, Redis)
• Familiarity with containerization and orchestration technologies (Docker, Kubernetes) for deploying and managing services at scale
• Excellent communication, presentation, and interpersonal skills, with ability to convey complex ML concepts to technical and non-technical stakeholders
• Experience with Large Language Models (LLMs) and generative AI applied to advertising, for example, using AI to generate ad copy, optimize creative content, or personalize messaging
• Experience designing and implementing agentic workflows that enable autonomous decision-making and real-time optimization in marketing campaigns
• Experience with machine learning model serving and optimization for real-time inference applications (latency-critical environments)
• Familiarity with modern data lake and table formats (e.g., Apache Iceberg, Apache Hudi) for managing large-scale analytical datasets
• Knowledge of microservices architecture and event-driven design patterns in distributed systems
• A track record of contributions to open-source projects or speaking at industry conferences, demonstrating thought leadership in AI/ML