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Staff Software Engineer

Zeta · Remote - United States

Location
Remote - United States
Salary
$160,000 - $180,000,
Funding
$430M • $2B val
Posted
May 28, 2026

Zeta is hiring a Staff Software Engineer based in Remote - United States. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.

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Role details

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 . The Role We're hiring multiple Staff Engineers to join a small, high-performance group supporting the engineering organizations behind Sailthru, LiveIntent, and WhatCounts — the three platforms that make up Zeta Actions, a business unit within Zeta Global. This is a deeply technical IC role. You'll spend most of your time in the code, the architecture, and the hard problems these teams run into as they build and operate platforms that serve brands and publishers at massive scale. Staff Engineers engage with teams in a few different ways — as a technical partner on complex projects, as a hands-on contributor to high-priority initiatives, or as an on-demand expert for teams that hit an architectural wall. The common thread is that you're someone teams want in the room when things get hard. Why This Role Real scale, real complexity — billions of messages, live traffic, meaningful performance and reliability constraints. Generative AI is part of how we work — we've adopted AI-assisted development across the engineering organization and expect our staff engineers to be fluent in these tools, not just aware of them. Active investment in AI engineering — you'll help shape how we build AI capabilities into the platform itself, not just use them in your own workflow. Real autonomy — you won't be handed a backlog. You'll be expected to find the important problems, make the case for solving them, and drive them to resolution. The best staff engineers here create their own leverage. Curiosity is a first-class trait on this team. We value engineers who read widely, ask uncomfortable questions, and aren't satisfied with how things work until they understand why. What You'll Do Technical Leadership Identify systemic engineering challenges across our platforms and drive their resolution — shaping the technical backlog and near-term architecture. Propose and validate technical approaches for problems involving scale, performance, security, or cross-team dependencies. Lead architectural decisions for complex, ambiguous, or high-risk initiatives. Incorporate modern industry patterns — including AI/ML tooling — into our technical strategy where it genuinely moves the needle. Hands-On Execution Write code, review PRs, debug production issues, and optimize system performance — this is not a whiteboard-only role. Dive deep into our AWS infrastructure, Kubernetes workloads, and JVM-based services to find and fix what's actually wrong. Participate in our on-call rotation as a second-level escalation point for complex engineering incidents. Step in during large incidents to help teams triage, coordinate, and resolve — and follow through with post-incident reviews that drive lasting fixes. Champion operational excellence across our engineering teams: observability, reliability, deployment practices, and the operational habits that keep systems healthy at scale. Cross-Team Influence Partner with engineering teams as a technical point of contact on complex projects — ensuring good architectural decisions get documented and don't have to be re-litigated. Work directly with Engineering Managers to align technical work with team and product priorities. Mentor engineers across our teams, raising the technical floor through reviews, pairing, and direct feedback. Stay close to the customer. Understand how Sailthru's platform affects the people using it, bring that context into technical decisions, and push back when engineering choices create friction for customers. Partner closely with the product team — contribute to shaping what gets built, not just how. The best technical decisions happen when engineering and product are thinking together from the start. What You'll Bring Deep, battle-tested expertise in AWS and cloud-native architecture — you know how to build reliable, scalable systems at real traffic volumes. Strong JVM fundamentals, primarily Java and Kotlin, with the range to work across languages as the problem demands. Hands-on Kubernetes experience — deploying, operating, and troubleshooting workloads in production. Experience designing and operating distributed systems — fault tolerance, consistency tradeoffs, service coordination, and failure modes at scale. Comfort working with large databases at scale — query optimization, schema design, operational concerns, and the tradeoffs between data storage approaches. Practical experience with AI/ML engineering — whether integrating LLM capabilities, building pipelines, or evaluating where AI actually helps vs. adds complexity. A track record of delivering large, complex initiatives in ambiguous environments — scoping, driving alignment, and shipping. Strong debugging instincts and a bias toward root-cause analysis over symptomatic fixes. The judgment to know when to push for the right solution and when pragmatism wins. Fluency with AI-driven development tools — Claude Code, GitHub Copilot, Codex, or similar — is a firm requirement. We use these tools daily and expect staff engineers to be effective with them and to model best practices around prompt discipline, output validation, and security. Comfort o

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