Senior Fullstack Software Engineer A.I.

Pebl•Toronto, ON

4d•CA$140,000 - CA$190,000

About The Position

Our mission at Pebl is to simplify global employment, enabling companies to hire, manage, and retain talent anywhere in the world. We’re not just adding a chatbot to our product — we’re rethinking what it means for software to work with you, not just for you. Our AI & Knowledge team is on the front lines of that shift, building Alfie, our conversational AI assistant, and we’re looking for a Senior Full-Stack Software Engineer who wants to help define what AI-native user experience looks like — not follow a playbook that already exists, because it doesn’t yet. The ideal candidate is a full-stack engineer who leans frontend — someone who understands how frontends and backends work together, but whose strongest instincts and sharpest skills are on the user-facing side. You’ve helped build products people genuinely love using, not just standard web applications, and you bring hands-on experience with LLM-based and agentic systems to the table. This role reports to the Engineering Manager, with day-to-day work led by our Staff Software Engineer.

Requirements

8+ years of professional software engineering experience, full-stack, with demonstrated strength and a clear preference for frontend/UX work.
1+ years of hands-on experience building LLM-powered or agentic products — not just calling an API, but designing how an agent reasons, uses tools, and hands control back to the user.
Experience building interfaces for AI-powered or agentic products — conversational UIs, copilots, assistants — including exposure to generative/dynamic UI techniques where the interface adapts to model behavior.
Solid understanding of LLM design patterns, prompt engineering, and observability/instrumentation for agentic systems.
Strong command of modern frontend (React/TypeScript) paired with a solid understanding of how frontends integrate with backend services, APIs, and real-time/streaming data.
A track record of helping build products beyond standard CRUD apps and dashboards — something with real craft, polish, or a distinctive user experience.
Comfort working across the full stack when needed (backend, infra) — this isn’t a pure frontend role.
A lean mindset — focus on delivering customer value by minimizing waste, optimizing workflows, fostering continuous improvement, and prioritizing quick iteration with quick feedback cycles.
Excitement for working in a space that’s evolving rapidly, where the best patterns for AI-driven UX are still being discovered — and a desire to help discover them.

Responsibilities

Design and build full-stack features for our conversational AI assistant, with particular depth and ownership on the frontend/UX side.
Apply hands-on experience with LLM-based and agentic systems — prompt design, tool/function calling, multi-step agent reasoning, evaluation — to shape both how the assistant behaves and how that behavior surfaces in the UI.
Bring product taste and a strong point of view on what makes an AI-driven experience great — push for a better UX rather than shipping “good enough.”
Push the boundaries of what conversational and generative AI interfaces can feel like — prototype and propose new interaction patterns as the underlying models and agent capabilities evolve.
Help shape the future of how people interact with software at Pebl — you’ll be working at the edge of what’s possible in conversational and agentic UX, not maintaining what’s already been figured out.
Build and maintain both frontend and backend components (TypeScript/React on the frontend; comfortable enough on the backend to design clean contracts and APIs that support agentic workflows).
Lead by example in clean, maintainable code across the stack; mentor junior team members and foster a culture of constant learning and improvement.
Contribute to technical discussions, design reviews, and code reviews to maintain high engineering standards.
Troubleshoot issues end-to-end (frontend and backend) in production, including AI-specific failure modes (e.g., bad model output, latency, dropped tool calls).