About the Company
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.
Role Snapshot
We’re hiring a hands-on Agentic AI engineer to build bidder-adjacent agents that improve campaign performance using RAG + tool-using workflows in a closed-loop ACE (Observe → Reason → Act → Evaluate). This is not a classic “train a model and deploy it” role—this is agentic decisioning + evaluation + safety in a low-latency AdTech environment.
What You’ll Do
- Build bidder-adjacent agentic workflows that recommend/execute campaign control actions (targeting constraints, budget & pacing levers, bid modifiers, supply/inventory selection, creative routing).
- Implement production-grade RAG: retrieval from policies/playbooks, campaign history, aggregates, and near-real-time telemetry; optimize grounding and reduce hallucinations.
- Create safe tool/action interfaces: idempotent execution, audit logs, dry-run + approval gates, rate limits, rollback/fallback behaviors.
- Own AgentOps: eval harnesses, regression suites, online experimentation (A/B), metrics tied to outcomes (CPA/ROAS, pacing, quality, margin).
- Add observability end-to-end (tracing prompts/retrieval/tool calls/latency) and reliability patterns (timeouts, circuit breakers, safe defaults).
- Partner with Backend/Bidding, Data Platform, DS/Optimization, Product, and SRE to define the boundary between deterministic per-request bidding and agent-driven control-plane decisions.
Must-Have Qualifications
- 5+ years building production backend and/or applied AI systems (design → ship → operate).
- Strong engineering in Java or Go (Python OK for evals/tooling).
- Hands-on experience with LLMs and agentic patterns (tool use, structured outputs, multi-step loops).
- Hands-on RAG experience (embeddings, hybrid retrieval, reranking, chunking, context assembly).
- Experience building evals and monitoring for AI systems (offline benchmarks + online experiments).
- Cloud + distributed systems fundamentals (APIs, microservices, streaming/eventing; AWS preferred).
Nice-to-Have
- Programmatic advertising (DSP/SSP/RTB) or other high-scale real-time decisioning domains.
- Experience with safety/policy enforcement for agent tools (prompt/tool sanitization, allowlists, schema validation).
- Experience designing “AI control plane” systems that influence production outcomes while keeping serving paths stable.
- Familiarity with experimentation platforms, feature stores, and large-scale telemetry pipelines.