首席产品经理
Principal Product Manager
Zeta Global (NYSE: ZETA) 是一家人工智能驱动的营销云平台，利用先进的人工智能（AI）和数万亿消费者信号，帮助营销人员更高效地获取、增长和留存客户。通过 Zeta 营销平台（ZMP），我们的愿景是通过将身份识别、智能分析和全渠道激活统一到一个平台中——由行业内最大的专有数据库和 AI 驱动——让复杂的营销变得简单。我们跨多个垂直领域的企业客户能够通过每个渠道为每位消费者提供个性化体验，从而提升营销活动的效果。Zeta 由 David A. Steinberg 和 John Sculley 于 2007 年创立，总部位于纽约市，并在全球设有办公室。了解更多，请访问 [www.zetaglobal.com](https://www.zetaglobal.com)。
### 岗位职责
- **代理链式与编排：** 开发并管理在复杂用例中连接 LLM 代理、工具、模型和工作流的架构。确保平台上的无缝编排、交接、状态管理和集成。
- **上下文图谱与上下文架构：** 领导开发一个共享的上下文图谱，使代理能够持续关注用户、品牌、账户、工作流、能力、数据、先前操作、目标和结果。定义上下文如何被捕捉、结构化、检索、治理，并在代理和产品之间可用。
- **上下文流服务：** 实现和管理上下文流服务，为代理提供对用户行为、应用状态、系统事件和相关业务数据的实时感知。确保上下文在多步骤工作流中保持最新、权限感知且可用。
- **代理遥测与可观测性：** 定义理解代理在生产环境中运行所需的遥测框架。对意图路由、代理和工具选择、上下文使用、交接、延迟、错误、完成率、置信度、用户干预和业务成果进行仪器检测和分析。构建必要的反馈循环以持续提升代理性能。
- **代理评估套件：** 构建强大的评估框架，用于测试代理质量、可靠性、路由、上下文使用、工具执行和端到端工作流完成情况。建立离线和生产评估方法，使可衡量的改进成为可能
查看英文原文
**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. To learn more, go to [www.zetaglobal.com](https://www.zetaglobal.com).
### Role Responsibilities
- **Agent Chaining and Orchestration:** Develop and manage the architecture for chaining LLM agents, tools, models, and workflows across complex use cases. Ensure seamless orchestration, handoffs, state management, and integration across the platform.
- **Context Graph and Context Architecture:** Lead the development of a shared Context Graph that gives agents persistent awareness of users, brands, accounts, workflows, capabilities, data, prior actions, goals, and outcomes. Define how context is captured, structured, retrieved, governed, and made available across agents and products.
- **Context Streaming Services:** Implement and manage context streaming services that provide agents with real-time awareness of user actions, application state, system events, and relevant business data. Ensure context remains current, permission-aware, and usable across multi-step workflows.
- **Agent Telemetry and Observability:** Define the telemetry framework required to understand how agents operate in production. Instrument and analyze intent routing, agent and tool selection, context utilization, handoffs, latency, errors, completion rates, confidence, user interventions, and business outcomes. Build the feedback loops necessary to continuously improve agent performance.
- **Agent Evaluation Suites:** Build robust evaluation frameworks for testing agent quality, reliability, routing, context utilization, tool execution, and end-to-end workflow completion. Establish both offline and production evaluation methodologies that enable measurable improvements over time.
- **Model Workbench Development:** Lead the creation of a Model Workbench designed for marketers and other non-technical users, enabling them to safely leverage LLMs, traditional ML, agents, and workflows without requiring deep technical expertise.
- **MCP Capability and Tool Registry:** Oversee the registration, documentation, governance, and discoverability of Model Context Protocol servers, tools, agents, and platform capabilities. Ensure capabilities are easy for both developers and agents to understand, select, and invoke correctly.
- **Cross-Functional Architecture and Organizational Alignment:** Drive alignment across Product, Engineering, Data Science, Design, Analytics, Security, and business stakeholders around shared agentic architecture, context standards, ownership models, evaluation criteria, and platform priorities. Establish clear accountability and operating models for capabilities that span multiple teams.
- **Platform Standards and Governance:** Define standards for how agents, tools, context sources, telemetry, and workflows are built and integrated across the organization. Balance local team autonomy with the consistency required to create a coherent platform experience.
- **Technical Troubleshooting and Prototyping:** Actively participate in troubleshooting and debugging using tools such as LangSmith and related observability platforms. Lead by example by rapidly building proof-of-concepts to validate technical approaches, identify architectural constraints, and demonstrate new product opportunities.
- **Advocacy for Rapid Iteration:** Promote a culture of rapid prototyping, experimentation, and evidence-based iteration. Use lightweight development and “vibe coding” where appropriate to quickly turn ideas into working experiences before investing in production-scale implementations.
### **Required Qualifications**
- **Product Management Experience:** Demonstrated experience leading complex technical products, particularly those involving LLMs, AI agents, workflow systems, developer platforms, ML infrastructure, or AI-driven applications.
- **Agentic Systems Expertise:** Strong understanding of LLM agents, tool use, orchestration, multi-agent workflows, state management, context management, and the architectural patterns required to operate agentic systems reliably at scale.
- **Context and Knowledge Architecture:** Experience designing or working with context graphs, knowledge graphs, semantic systems, memory architectures, metadata platforms, or other systems that allow applications and models to understand relationships between users, data, actions, and business objects.
- **Telemetry and Observability:** Strong understanding of instrumentation, telemetry, evaluation, and observability for complex software or AI systems. Ability to define the signals required to distinguish between model failures, orchestration failures, context failures, tool failures, and UX failures.
- **Organizational Alignment and Influence:** Exceptional ability to align senior stakeholders and cross-functional teams around shared technical architecture, product priorities, ownership boundaries, and operating standards. Comfortable leading initiatives where no single team controls the entire outcome.
- **Systems Thinking:** Ability to reason across product experience, model behavior, data, infrastructure, APIs, organizational ownership, and operational processes rather than optimizing individual components in isolation.
- **Technical Proficiency:** Strong technical background with hands-on familiarity with tools such as LangSmith and experience working with APIs, workflow orchestration, LLM agent chaining, MCP, evaluation frameworks, and modern AI development environments.
- **Problem-Solving and Prototyping:** Demonstrated ability to troubleshoot ambiguous technical problems, rapidly prototype potential solutions, and translate experimentation into scalable product and architectural decisions.
- **Communication and Collaboration:** Excellent communication skills with the ability to translate highly technical concepts into clear product strategies, operating models, and decisions for technical and non-technical audiences.
### Preferred Qualifications
- Experience building or operating **agentic infrastructure, AI platforms, context platforms, knowledge graphs, or developer ecosystems**.
- Experience integrating **traditional machine learning with generative models and agentic systems**, including using predictive models as tools or contextual inputs for agents.
- Experience with **workflow orchestration platforms** and distributed systems involving multiple services, teams, and execution environments.
- Experience designing **AI telemetry, evaluation systems, experimentation frameworks, or production observability** for LLM-powered products.
- Demonstrated success establishing **cross-functional technical standards and governance** across multiple engineering and product organizations.
- Experience building systems where **context, telemetry, and evaluation form a continuous learning loop**, allowing agent behavior and product experiences to improve based on real-world usage.
**BENEFITS & PERKS**
- Unlimited PTO
- Excellent medical, dental, and vision coverage
- Employee Equity
- Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!
**SALARY RANGE**
The salary range for this role is $185,000 - $205,000, depending on location and experience.
**PEOPLE & CULTURE AT ZETA**
Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.
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/](https://zetaglobal.com/blog/a-look-into-zetas-ergs/)
**ZETA IN THE NEWS!**
[https://zetaglobal.com/press/?cat=press-releases](https://zetaglobal.com/press/?cat=press-releases)
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