Staff Product Engineer, Enterprise AI

Mozilla

3d•$185,000 - $220,000•Remote

About The Position

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.

Requirements

10+ years of professional software engineering experience, including significant experience building and shipping production applications with Staff-level scope, leadership, and impact.
Strong full-stack engineering skills, with the ability to work across user interfaces, APIs, services, integrations, data flows, and infrastructure-adjacent areas.
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.
Practical experience evaluating and using multiple AI tools, models, frameworks, or development harnesses, with the ability to explain tradeoffs, limitations, and preferred tooling choices.
Experience working with open-source enterprise products, developer tools, infrastructure products, platform products, or technically complex enterprise software.
Strong product judgment, including the ability to translate product direction, user feedback, partner input, customer needs, and product signals into practical technical plans and follow-up iterations.
Comfort operating independently in a fast-changing product environment where product direction, technical architecture, and enterprise requirements are still being defined.
Strong understanding of software quality, testing, maintainability, security, performance, reliability, and implementation tradeoffs.
Strong collaboration and communication skills across engineering, product, design, open-source, and customer- or partner-facing stakeholders.
Awareness of enterprise software needs, including privacy, security, administration, access control, compliance, deployment flexibility, supportability, and responsible AI considerations.

Nice To Haves

Experience with self-hosted, hybrid, customer-controlled, or regulated deployment environments.
Familiarity with regulatory or compliance considerations affecting AI and enterprise software, such as the EU AI Act, Cyber Resilience Act, GDPR, HIPAA, SOC 2, or similar frameworks.
Experience working with enterprise customers, public sector organizations, systems integrators, technical partners, or customer-facing engineering teams.
Experience contributing to reusable product patterns, component libraries, technical frameworks, or shared engineering practices.
Experience with instrumentation, observability, experimentation, or other approaches to understanding product adoption, reliability, and value.

Responsibilities

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.
Write clear, maintainable, well-tested code across front-end, back-end, APIs, integrations, and infrastructure-adjacent areas.
Help establish reusable product and engineering patterns that allow future features to be built more efficiently.
Contribute to a product engineering culture grounded in ownership, quality, experimentation, user value, and practical execution.