Principal AI/ML Engineer, Zeta Global

• 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

Desirable

• As a Principal AI/ML Engineer in our AdTech team, you will be a key individual contributor driving the development of advanced machine learning models and AI-driven features for our advertising platform
• You will design, build, and deploy ML solutions for campaign optimization, user personalization, and creative content generation, operating at large scale and low latency to handle billions of ad events per day
• This role requires deep expertise in machine learning techniques and the programmatic advertising ecosystem (e.g., real-time bidding and digital marketing data)
• You will work closely with engineering, product, and data science teams to ensure our ML systems are highly performant, scalable, and reliable
• You will also spearhead innovation by leveraging large language models (LLMs) and intelligent agent architectures to create new capabilities that automate and enhance advertising campaigns
• Machine Learning Leadership: 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
• ML System Design: 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
• Technical Strategy: 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
• AI & Agentic Applications: 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
• Cross-Functional Collaboration: 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
• Performance & Reliability: 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
• Mentorship & Best Practices: 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