Principal AI Engineer

• Technical strategy and architecture. Define the AI/ML architecture and technical roadmap across the portfolio. Identify shared capabilities, establish clear interfaces, and guide build-versus-buy decisions while respecting product-specific requirements.
• Predictive ML and optimization. Lead advances in prediction, targeting, ranking, bidding, pacing, and personalization. Guide feature development, training, calibration, and serving, addressing sparse signals, delayed outcomes, and changing data distributions.
• Agentic frameworks and agents. Design reusable orchestration, tool integration, retrieval, state and memory, and multi-agent coordination. Build workflows for campaign planning, insights, recommendations, and execution, with explicit permissions, human approval where needed, and recoverable failure handling.
• Portfolio integration. Partner with Predict, Agentic Frameworks, Email DSP/SSP, and agentic teams to connect models, data, and services through well-defined APIs and shared evaluation practices. Turn successful patterns into reusable components and practical migration paths.
• Production engineering. Own critical designs and implementations across data pipelines, training, deployment, and inference. Establish MLOps and LLMOps practices for reproducibility, versioning, automated testing, observability, and safe rollouts and rollbacks.
• Evaluation and business impact. Define offline benchmarks and online experiments tied to advertiser outcomes. Evaluate prediction quality, calibration, agent task completion, tool accuracy, latency, and cost; use A/B testing and incrementality methods where appropriate.
• Reliability and responsible AI. Set standards for service performance, drift detection, data quality, and incident response. Build privacy-aware data access, auditability, and protection against prompt injection, data leakage, and unintended agent actions.
• Technical leadership. Partner with engineering, data science, product, and business leaders to turn ambiguous needs into deliverable plans. Lead design and code reviews, mentor senior engineers, and resolve technical decisions across teams.

• 12+ years of relevant software engineering and applied ML experience, or equivalent demonstrated expertise, including substantial ownership of production ML systems and technical leadership across multiple teams.
• Deep expertise in predictive ML for ranking, recommendation, classification, or optimization, with strong foundations in statistics, experimentation, model evaluation, and the full model lifecycle.
• Hands-on experience delivering LLM applications and agentic workflows to production, including tool use, retrieval, orchestration, evaluation, guardrails, and operational monitoring.
• Proven ability to architect distributed, high-throughput, low-latency systems and make sound trade-offs across model quality, infrastructure cost, and operational complexity.
• Strong Python skills and proficiency in Java, Go, or a comparable backend language; experience with PyTorch or TensorFlow, SQL, and large-scale data or streaming systems such as Spark and Kafka.
• Experience with cloud infrastructure, preferably AWS, containerization, CI/CD, model serving, and production observability; familiarity with relational, NoSQL, caching, and retrieval systems.
• Ability to influence without direct authority, communicate architectural decisions clearly, mentor experienced engineers, and translate business objectives into measurable technical outcomes.

• AdTech experience across DSPs, SSPs, real-time bidding, audience modeling, identity, attribution, or measurement; relevant experience in recommendation systems or marketplaces is also valued.
• Experience with causal inference, uplift modeling, contextual bandits, constrained optimization, or online learning in production.
• Experience with privacy-aware identity and audience systems, email advertising, or integrating ML capabilities across multiple products.
• Experience optimizing model and LLM inference, selecting and adapting models, or building reusable ML and agent platforms adopted by multiple teams.

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/

ZETA IN THE NEWS!

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Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI.

Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.