Staff Software Engineer, Product

Hi, I'm Paul, VP of Engineering at Customer.io. We're looking for a staff product engineer — not a generalist who happens to touch frontend and backend, but someone who thinks like a product owner and ships like an engineer.

Over 9,000 companies — from scrappy startups to global brands — use our platform to send billions of emails, push notifications, in-app messages, and SMS every day. Customer.io powers automated communication that people actually want to receive.

We help teams send smarter, more relevant messages using real-time behavioral data. Under the hood: Go, React, and AI help us ship fast and scale with confidence.

Own important, ambiguous problems from initial framing through launch and iteration. Set direction, align partners, and stay accountable for the result.
Partner with product, design, and data to shape priorities, scope the work, and define how success will be measured.
Lead technical decisions across the stack, including architecture, performance, reliability, and maintainability.
Lead work across teams and systems. Build alignment, resolve technical disagreements, and keep complex initiatives moving.
Improve engineering practices through mentorship, reusable patterns, and clear technical guidance.
Use AI throughout your engineering work, with sound judgment about where it helps, where it falls short, and how its output should be reviewed. Help the team improve by testing new workflows, sharing what works, and identifying useful opportunities for automation.
Participate in an on-call rotation as required, supporting the systems the team builds.

A track record of leading complex work across teams or systems and delivering meaningful customer, business, or engineering outcomes.
Strong backend engineering experience, with the ability to work across the stack and lead end-to-end technical decisions.
Strong product and technical judgment, especially when the problem or path forward is unclear.
The ability to influence experienced peers and align teams without formal authority.
Experience using metrics, experimentation, and observability to guide decisions and improve production systems.
Regular, hands-on use of AI across the software development lifecycle, with the judgment to adapt as tools change while remaining accountable for quality, security, and outcomes. Experience improving how a broader team uses AI is a strong plus.