Lead AI Engineer

About Who We Are 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. The Role The Zeta AI R&D team sits at the heart of our technology and delivers on Zeta Global’s core brand promise: state-of-the-art marketing technology innovation. What You Will Do Zeta Global’s technology platform supports a real-time bidding system handling over 100 billion events per day, a reporting system aggregating and analyzing terabytes of data in real time, and a learning system applying machine learning and AI techniques to more than 40 petabytes of data. Together, these systems ensure Zeta serves the right advertisement to the right user at the right time. At the core of this platform is our Artificial Intelligence and Machine Learning team, which builds the models and decision-making systems that power Zeta’s products. This role is focused on post-training and alignment of large language models , with an emphasis on Supervised Fine-Tuning (SFT) , preference optimization, and production operation of agentic LLM systems. As a Lead AI Engineer, you will own the end-to-end post-training lifecycle of LLMs —from SFT data curation and training strategy through evaluation, deployment, monitoring, and iteration—using platforms such as OpenAI and AWS Bedrock. Specifically, you will: Lead Supervised Fine-Tuning (SFT) of large language models in production, shaping instruction-following, reasoning quality, tone, and domain-specific behavior Extend SFT pipelines with instruction tuning and preference-based optimization (e.g., RLHF-style approaches or direct preference optimization) Design, curate, and maintain high-quality SFT and preference datasets , combining human-labeled and synthetic data tailored to real-world marketing and decisioning use cases Own model evaluation and benchmarking , including: Offline behavioral evals (instruction adherence, reasoning depth, hallucination rates) Online experiments and A/B tests Continuous regression detection and performance monitoring Develop and operate agentic LLM systems , enabling multi-step reasoning, tool use, workflow orchestration, and decision execution Implement and optimize prompting, retrieval-augmented generation (RAG), memory, and tool-calling strategies , with a clear understanding of when to solve problems via SFT versus prompting Partner closely with data engineering, platform, and product teams to integrate fine-tuned models into high-throughput, low-latency systems Establish best practices for LLM versioning, experimentation, deployment, rollback, governance, and safety Provide technical leadership and mentorship to engineers working on applied AI and LLM systems Who You Are You are deeply interested in how LLM behavior changes through SFT and post-training , especially in real production environments. You enjoy improving models through data, evaluation, and iteration—not just prompt engineering. You take ownership of outcomes, from model quality and alignment to reliability, latency, and cost. Required Experience and Skills Significant hands-on experience with Supervised Fine-Tuning (SFT) of LLMs in production , beyond prompt-only approaches Direct experience using OpenAI APIs and/or AWS Bedrock for SFT, post-training, and deployment Strong understanding of LLM post-training workflows , including data preparation, instruction tuning, evaluation methodologies, and common failure modes Experience building and operating agentic LLM systems (tool use, multi-step reasoning, workflow orchestration) Proficiency in Python and modern ML frameworks (e.g., PyTorch ) Experience operating ML systems in distributed, production environments Strong intuition for trade-offs between model quality, latency, cost, safety, and scalability #J-18808-Ljbffr