Staff Product Engineer

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. We are a small but growing company of 60+ people distributed across seven countries.

• 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.
• Collaborate with senior technical leadership to turn technical direction into executable product work.
• Use AI-assisted development tools thoughtfully.
• Make thoughtful technical and product tradeoffs in ambiguous situations.
• Follow through after launch by reviewing usage, feedback, and adoption patterns.
• Instrument features or collaborate on measurement approaches.
• Identify product gaps, technical risks, edge cases, and opportunities for improvement.
• Write clear, maintainable, well-tested code across front-end, back-end, APIs, integrations, and infrastructure-adjacent areas.
• Help establish reusable product and engineering patterns.
• Contribute to a product engineering culture grounded in ownership, quality, experimentation, and user value.

• 10+ years of professional software engineering experience, including significant experience building and shipping production applications with Staff-level scope.
• Strong full-stack engineering skills.
• Demonstrated experience owning complex product features from technical planning through implementation, launch, evaluation, and iteration.
• Experience building AI-enabled products or features, with practical familiarity in LLMs, RAG, agents, model/provider abstraction, data connectors, retrieval systems, or AI application architecture.