Staff Product Engineer, Enterprise AI

Why Thunderbird?

Thunderbird is one of the world’s most trusted open-source email applications, empowering more than 20 million people globally. At MZLA, the team behind Thunderbird, we build privacy-respecting communication and productivity tools that help people manage their digital lives while staying in control of their data.

We are open source by design. Our products are developed transparently and in collaboration with a global community of contributors, and that same spirit shapes how we work: collaboratively, inclusively, and with a shared commitment to putting users first.

We are a small but growing company of 60+ people distributed across seven countries. As Thunderbird evolves beyond a single desktop application, we are expanding across desktop, mobile, paid services, and new product experiences that help people and organizations work more effectively without giving up control to big tech ecosystems.

Our revenue model combines user donations, which help keep Thunderbird’s apps freely available, with paid services that cover the costs of hosted offerings while supporting long-term sustainability and continued innovation.

A note about MZLA and Mozilla: MZLA Technologies Corporation is the nonprofit-owned company behind Thunderbird and part of the Mozilla family. MZLA is separate from Mozilla Corporation and has its own compensation programs, benefits, and employment policies.

To learn more, visit www.thunderbird.net and www.thunderbolt.io.

The opportunity

Thunderbolt is an open-source, cross-platform AI product initiative from the team behind Thunderbird. It is being built around a clear principle: people and organizations should be able to use AI while staying in control of their data, infrastructure, model choices, and long-term options. Thunderbolt is designed to support flexible deployment models, integrations with AI tools and workflows, and use cases across desktop, web, and mobile.

We’re looking for a Staff Product Engineer to help build Thunderbolt as it moves from early product development into enterprise use cases. This role sits at the intersection of full-stack product engineering, AI-enabled product development, enterprise workflows, technical execution, product judgment, and post-launch iteration.

You’ll work across the stack to turn product direction, user needs, partner feedback, and enterprise requirements into complete product features for AI-powered chat, search, research, workflow automation, administration, integrations, and enterprise workflows. This is not a role focused only on implementing tickets. You’ll own product work from concept through launch, adoption, and iteration, helping the team understand whether features are usable, reliable, being used, and creating value.

You should be comfortable operating independently in ambiguity, working across front-end, back-end, APIs, integrations, and infrastructure-adjacent areas, and making thoughtful tradeoffs between speed, usability, maintainability, reliability, and enterprise needs. Strong candidates will be able to turn ambiguous product ideas and technical direction into shipped product experiences, reusable product and engineering patterns, and practical improvements that help Thunderbolt scale as both an open-source product and an enterprise-ready platform.

What you’ll do

Own the implementation of Thunderbolt product features from concept through launch, adoption, and iteration.

Translate product vision, user needs, partner feedback, and enterprise requirements into practical technical plans and shipped product experiences.

Work across the stack to build, integrate, test, and ship complete product features.

Build features for AI-powered chat, search, research, workflow automation, administration, integrations, and enterprise workflows.

Partner closely with product, design, engineering, enterprise-facing teams, and open-source contributors to clarify requirements and deliver high-quality outcomes.

Collaborate with senior technical leadership to turn product-shaping technical direction into executable product work.

Use AI-assisted development tools thoughtfully to accelerate implementation while maintaining strong judgment around quality, security, maintainability, and reliability.

Make thoughtful technical and product tradeoffs in ambiguous situations, balancing speed, usability, maintainability, reliability, and enterprise needs.

Follow through after launch by reviewing usage, qualitative feedback, support signals, quality issues, and adoption patterns.

Instrument features or collaborate on measurement approaches that help the team understand adoption, reliability, and product value.

Identify product gaps, technical risks, edge cases, and opportunities to improve the user experience.