Senior Staff AI & Agentic Systems Engineer
Mozilla
Role Description
Mozilla is building a new browser infrastructure platform for the new generation of web automation. We are looking for an experienced Staff Engineer to join our early-stage team at Mozilla to lead the architectural design and implementation of our next-generation AI agent frameworks and workflows. This is a "ground-up" role within a fast-moving, nimble team, making it a perfect fit for someone with early-stage startup experience who thrives on a blank slate. You will be the technical anchor for our AI engineering efforts, establishing the patterns for how we build, evaluate, and scale autonomous capabilities.
This role sits within Mozilla’s New Products organization, which operates as an internal incubator for high-potential ventures. You will join a fast-paced autonomous team building a new product from its earliest stages.
What You’ll Do
- Architect Agentic Systems from Zero:
- Design and deploy production-grade multi-agent frameworks, complex orchestration layers, and custom AI developer tooling.
- Balance Speed and Scale:
- Navigate the tension between rapid prototyping and long-term sustainability, knowing exactly when to cut corners to validate a concept and when to slow down to lay rock-solid architectural foundations.
- Orchestrate LLM Workflows:
- Navigate and integrate various commercial and open-source large language models, selecting and optimizing the right models for specific agentic behaviors.
- Accelerate with AI Tooling:
- Lead by example by extensively leveraging advanced AI coding tools to maximize velocity, while establishing best practices for AI-assisted engineering across the team.
- Drive Evaluation and Performance:
- Implement rigorous frameworks for benchmarking, prompt engineering, and evaluating agent reliability, latency, and accuracy.
- Collaborative Technical Leadership:
- Set engineering standards for code quality and system performance, mentoring other engineers and acting as a primary bridge between research and product.
Qualifications
- Deep Experience: 7+ years of professional software engineering experience, particularly in fast-paced environments where you've shipped complex systems from scratch.
- Pragmatic Architecture: A proven ability to judge technical debt. You know how to ship an MVP in days, but you also know how to design a clean API boundary so that early speed doesn't block future scale.
- Agentic Mastery: 2+ years of direct, hands-on production experience specifically building autonomous workflows, custom tools, and agentic systems.
- LLM Fluency: Deep familiarity with diverse LLMs and orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, or similar tools).
- Startup Mentality: Prior early-stage startup experience, meaning you are comfortable with ambiguity, pivot quickly based on data, and focus heavily on execution.
- AI-Native Workflow: Deep, everyday proficiency building software with advanced AI coding tools (such as Cursor, Copilot, or custom LLM extensions) to radically speed up delivery.
- Strong Foundations: Comfort writing clean, high-performance code in languages like Python or Go, with a solid grasp of distributed backend systems.
Bonus Points For
- Experience building developer-facing products, SDKs, or open-source libraries.
- Prior contributions to open-source AI or agent frameworks.
- Experience with vector databases and retrieval-augmented generation (RAG) at scale.
Benefits
- Generous performance-based bonus plans to all eligible employees – we share in our success as one team.
- Rich medical, dental, and vision coverage.
- Generous retirement contributions with 100% immediate vesting (regardless of whether you contribute).
- Quarterly all-company wellness days where everyone takes a pause together.
- Country-specific holidays plus a day off for your birthday.
- One-time home-office stipend.
- Annual professional development budget.
- Quarterly well-being stipend.
- Considerable paid parental leave.
- Employee referral bonus program.
- Other benefits (life/AD&D, disability, EAP, etc. – varies by country).
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