About this role
Engineers who thrive on building resilient, high-throughput distributed systems and tackling complex agentic AI challenges will find a natural home in this position. Zeta Global is looking for a hands-on Staff Software Engineer to provide technical leadership for forecasting and recommendations platforms, with an emphasis on production-grade AI applications.
The team builds and operates mission-critical SaaS services, data pipelines, and infrastructure that power AI-driven decisioning at scale. This person will design and maintain low-latency APIs, agent orchestration tools, and foundational LLM integrations while managing end-to-end service reliability and participating in incident response.
The role exists to drive technical direction and ensure operational excellence across core platforms as the company expands its marketing technology offerings. An ideal candidate brings extensive software engineering experience, a background in scaling cloud-native distributed systems, and the ability to balance technical constraints with pragmatic product execution.
What you will do
- Design, build, and operate high-throughput, low-latency distributed systems supporting forecasting, recommendations, and agentic AI workflows in production.
- Write production-quality code daily and own services end-to-end from design through on-call rotation and incident resolution.
- Architect SaaS services including APIs, data pipelines, model inference, and agent orchestration tools using foundational LLMs.
- Embrace AI-assisted development workflows like Cursor and GitHub Copilot to move quickly while enforcing production discipline.
- Champion observability and reliability through metrics, logging, tracing, alerting, and post-incident analysis.
- Identify architectural risks early and reduce complexity across services, infrastructure, and processes to improve stability and team velocity.
- Provide technical guidance across teams, set short- to medium-term technical direction, and participate in architectural reviews.
What you need
- Bring 12+ years of professional software engineering experience building and operating production-grade distributed systems.
- Demonstrate a strong track record of hands-on ownership of business-critical services with measurable improvements in latency, throughput, stability, or cost.
- Possess deep expertise in systems design, service boundaries, concurrency, data modeling, failure handling, and scalability tradeoffs.
- Have production experience supporting machine learning-driven systems, including serving, pipelines, and infrastructure.
- Show expert-level experience with AWS alongside hands-on proficiency with Kubernetes, containerized microservices, and modern CI/CD pipelines.
- Hold a bachelor’s or master’s degree in computer science, Mathematics, or a related field, or possess equivalent practical experience.
Skills and tech stack
Questions candidates ask
What kind of systems and projects will I work on?
You will design, build, and operate high-throughput, low-latency distributed systems that power forecasting, recommendations, and AI-driven decisioning at scale. This includes building production-grade agentic applications, tool-using agents, workflow orchestration, and foundational LLM integrations.
What tools and technologies are used in this role?
The tech stack includes AWS, Kubernetes, modern CI/CD pipelines, containerized microservices, and backend languages such as Python, Java, Go, or Scala. You will also work with foundational LLMs like GPT, Claude, and Gemini Pro, along with AI-assisted development tools like Cursor and GitHub Copilot.
What are the expectations for a Staff-level engineer here?
You are expected to be a prolific, hands-on contributor who raises the bar through example and owns large portions of the codebase and system architecture. Responsibilities include setting 6-to-12-month technical direction, simplifying complex systems, and influencing standards across teams.